Driver-Vehicle Automation Collaboration

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 69098

Special Issue Editors

School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: vehicle system dynamics; driver-vehicle automation collaboration
Special Issues, Collections and Topics in MDPI journals
Associate Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: automated driving; human–machine systems; intelligent electric vehicles; human–robot collaboration; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA
Interests: autonomous driving; human-robot interaction; behavior prediction and motion planning
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China
Interests: human-vehicle cooperative steering control; intelligent vehicle motion control; advanced control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Before realizing fully autonomous driving, highly automated vehicles will play a significant role in the development of vehicle intelligence technologies. Highly automated driving presents an exciting new development in vehicle technology, however, in the meantime it poses a new challenge, namely how to ensure a safe, smart, and smooth interactions between human driver and automation functionality. Therefore, a better understanding of the interaction between human drivers and automation systems becomes a key issue to the realization of effective and efficient driver-automation collaboration for automated driving.

The special session aims to provide up-to-date research concepts, theoretical findings and practical solutions that could help implement the interaction between human driver and vehicle automation. Papers are invited in all these areas (but are not limited to them), as they are multidisciplinary topics involving economic and vehicle aspects as well. Both theoretical and experimental works are welcome, especially those including validation with real-world data or experiments. Recently, interest in information fusion, decision making, traffic optimization has been raised; therefore, papers exploring the utility of vehicle dynamic control in these topics are also encouraged.

Prof. Dr. Yahui Liu
Prof. Dr. Chen Lv
Dr. Liting Sun
Prof. Dr. Jian Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vehicle system dynamics
  • driver-automation shared control
  • driver interactions with vehicle automation
  • vehicle dynamics control strategies
  • models, simulators, and testbeds for driver–vehicle systems
  • application of AI and machine learning for driver–vehicle systems
  • driver cognitive behaviors: strategy, trust, learning, and errors
  • driver actuation behaviors: neuromuscular dynamics and delay
  • driver perception behaviors: preview, haptic, and vestibular sensing
  • automated driving and autonomous vehicles

Published Papers (23 papers)

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19 pages, 6176 KiB  
Article
Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs
by Di Wu, Shuang Z. Tu, Robert W. Whalin and Li Zhang
Vehicles 2023, 5(4), 1275-1293; https://doi.org/10.3390/vehicles5040070 - 26 Sep 2023
Cited by 1 | Viewed by 820
Abstract
Detecting drivers’ cognitive states poses a substantial challenge. In this context, cognitive driving anomalies have generally been regarded as stochastic disturbances. To the best of the author’s knowledge, existing safety studies in the realm of human Driving Anomaly Detection (DAD) utilizing vehicle trajectories [...] Read more.
Detecting drivers’ cognitive states poses a substantial challenge. In this context, cognitive driving anomalies have generally been regarded as stochastic disturbances. To the best of the author’s knowledge, existing safety studies in the realm of human Driving Anomaly Detection (DAD) utilizing vehicle trajectories have predominantly been conducted at an aggregate level, relying on data aggregated from multiple drivers or vehicles. However, to gain a more nuanced understanding of driving behavior at the individual level, a more detailed and granular approach is essential. To bridge this gap, we developed a Data Anomaly Detection (DAD) model designed to assess a driver’s cognitive abnormal driving status at the individual level, relying solely on Basic Safety Message (BSM) data. Our DAD model comprises both online and offline components, each of which analyzes historical and real-time Basic Safety Messages (BSMs) sourced from connected vehicles (CVs). The training data for the DAD model consist of historical BSMs collected from a specific CV over the course of a month, while the testing data comprise real-time BSMs collected at the scene. By shifting our focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can significantly contribute to a more comprehensive comprehension of driving behavior. Furthermore, when combined with a Conflict Identification (CIM) model, the DAD model has the potential to enhance the effectiveness of Advanced Driver Assistance Systems (ADAS), particularly in terms of crash avoidance capabilities. It is important to note that this paper is part of our broader research initiative titled “Automatic Safety Diagnosis in the Connected Vehicle Environment”, which has received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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17 pages, 1818 KiB  
Article
Driving with a Haptic Guidance System in Degraded Visibility Conditions: Behavioral Analysis and Identification of a Two-Point Steering Control Model
by Yishen Zhao, Philippe Chevrel, Fabien Claveau and Franck Mars
Vehicles 2022, 4(4), 1413-1429; https://doi.org/10.3390/vehicles4040074 - 15 Dec 2022
Viewed by 1603
Abstract
The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms [...] Read more.
The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms of driving performance as well as through the identification of anticipation and compensation parameters of the driver model. The hypothesis is that if the structure of the model is valid in the considered conditions, the value of the parameters will change in coherence with the observed behavior. The results of an experiment conducted on a driving simulator demonstrate that the identified model can account for the cumulative influence of the haptic guidance system and degraded visibility. The anticipatory gain is sensitive to changes in driving conditions that have a direct influence on the produced trajectory, and the compensatory gain is sensitive to a decrease in the variability of the lateral position. However, a model with only the steering wheel angle as output is not able to determine whether the change in lateral position variability is due to the driver’s lack of anticipation or to the assistance provided by the haptic guidance system. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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18 pages, 1118 KiB  
Article
Adaptive Driving Style Classification through Transfer Learning with Synthetic Oversampling
by Philippe Jardin, Ioannis Moisidis, Kürsat Kartal and Stephan Rinderknecht
Vehicles 2022, 4(4), 1314-1331; https://doi.org/10.3390/vehicles4040069 - 15 Nov 2022
Viewed by 1580
Abstract
Driving style classification does not only depend on objective measures such as vehicle speed or acceleration, but is also highly subjective as drivers come with their own definition. From our perspective, the successful implementation of driving style classification in real-world applications requires an [...] Read more.
Driving style classification does not only depend on objective measures such as vehicle speed or acceleration, but is also highly subjective as drivers come with their own definition. From our perspective, the successful implementation of driving style classification in real-world applications requires an adaptive approach that is tuned to each driver individually. Within this work, we propose a transfer learning framework for driving style classification in which we use a previously developed rule-based algorithm for the initialization of the neural network weights and train on limited data. Therefore, we applied various state-of-the-art machine learning methods to ensure robust training. First, we performed heuristic-based feature engineering to enhance generalized feature building in the first layer. We then calibrated our network to be able to use its output as a probabilistic metric and to only give predictions above a predefined neural network confidence. To increase the robustness of the transfer learning in early increments, we used a synthetic oversampling technique. We then performed a holistic hyperparameter optimization in the form of a random grid search, which incorporated the entire learning framework from pretraining to incremental adaption. The final algorithm was then evaluated based on the data of predefined synthetic drivers. Our results showed that, by integrating these various methods, high system-level performance and robustness were met with as little as three new training and validation data samples in each increment. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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36 pages, 5733 KiB  
Article
Improved Technique for Autonomous Vehicle Motion Planning Based on Integral Constraints and Sequential Optimization
by Maksym Diachuk and Said M. Easa
Vehicles 2022, 4(4), 1122-1157; https://doi.org/10.3390/vehicles4040060 - 12 Oct 2022
Cited by 2 | Viewed by 1740
Abstract
The study is dedicated to elaborating and analyzing a technique for autonomous vehicle (AV) motion planning based on sequential trajectory and kinematics optimization. The proposed approach combines the finite element method (FEM) basics and nonlinear optimization with nonlinear constraints. There were five main [...] Read more.
The study is dedicated to elaborating and analyzing a technique for autonomous vehicle (AV) motion planning based on sequential trajectory and kinematics optimization. The proposed approach combines the finite element method (FEM) basics and nonlinear optimization with nonlinear constraints. There were five main innovative aspects introduced in the study. First, a 7-degree polynomial was used to improve the continuity of piecewise functions representing the motion curves, providing 4 degrees of freedom (DOF) in a node. This approach allows using the irregular grid for roadway segments, increasing spans where the curvature changes slightly, and reducing steps in the vicinity of the significant inflections of motion boundaries. Therefore, the segment length depends on such factors as static and moving obstacles, average road section curvature, camera sight distance, and road conditions (adhesion). Second, since the method implies splitting the optimization stages, a strategy for bypassing the moving obstacles out of direct time dependency was developed. Thus, the permissible area for maneuvering was determined using criteria of safety distance between vehicles and physical limitation of tire–road adhesion. Third, the nodal inequality constraints were replaced by the nonlinear integral equality constraints. In contrast to the generally distributed approach of restricting the planning parameters in nodes, the technique of integral equality constraints ensures the disposition of motion parameters’ curves strictly within the preset boundaries, which is especially important for quite long segments. In this way, the reliability and stability of predicted parameters are improved. Fourth, the seamless continuity of both the sought parameters and their derivatives is ensured in transitional nodes between the planning phases and adjacent global coordinate systems. Finally, the problem of optimization rapidity to match real-time operation requirements was addressed. For this, the quadrature integration approach was implemented to represent and keep all the parameters in numerical form. The study considered cost functions, limitations stipulated by the vehicle kinematics and dynamics, as well as initial and transient conditions between the planning stages. Simulation examples of the predicted trajectories and curves of kinematic parameters are demonstrated. The advantages and limitations of the proposed approach are highlighted. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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18 pages, 5646 KiB  
Article
Using Active Seat Belt Retractions to Mitigate Motion Sickness in Automated Driving
by Christina Kremer, Markus Tomzig, Nora Merkel and Alexandra Neukum
Vehicles 2022, 4(3), 825-842; https://doi.org/10.3390/vehicles4030046 - 11 Aug 2022
Cited by 1 | Viewed by 2042
Abstract
The introduction of automated-driving functions provides passengers with the opportunity to engage in non-driving related tasks during the ride. However, this benefit might be compromised by an increased incidence of motion sickness. Therefore, we investigated the effectiveness of active seat belt retractions as [...] Read more.
The introduction of automated-driving functions provides passengers with the opportunity to engage in non-driving related tasks during the ride. However, this benefit might be compromised by an increased incidence of motion sickness. Therefore, we investigated the effectiveness of active seat belt retractions as a countermeasure against motion sickness during inattentive automated driving. We hypothesized that seat belt retractions would mitigate motion sickness by supporting passengers to anticipate upcoming braking maneuvers, by actively tensioning their neck muscles and, thereby, reducing the extent of forward head movement while braking. In a motion base driving simulator, 26 participants encountered two 30 min automated drives in slow-moving traffic: one drive with active seat belt retractions before each braking maneuver and a baseline drive without. The results revealed that there was no difference in perceived motion sickness between both experimental conditions. Seat belt retractions resulted in an increased activity of the lateral neck muscles and supported drivers to anticipate braking maneuvers. However, at the same time, the retractions led to an increased magnitude of head movement in response to braking. This research lays the groundwork for future research on active seat belt retractions as a countermeasure against motion sickness and provides directions for future work. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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17 pages, 17535 KiB  
Article
Velocity Prediction Based on Map Data for Optimal Control of Electrified Vehicles Using Recurrent Neural Networks (LSTM)
by Felix Deufel, Purav Jhaveri, Marius Harter, Martin Gießler and Frank Gauterin
Vehicles 2022, 4(3), 808-824; https://doi.org/10.3390/vehicles4030045 - 11 Aug 2022
Cited by 4 | Viewed by 2055
Abstract
In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all [...] Read more.
In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all traction torque-relevant variables for such strategies. The extension primarily includes the proper utilization of map data in the case of an a priori known route. Approaches from Artificial Intelligence (AI) have proven to be effective for such proposals. With regard to this, Recurrent Neural Networks (RNN) are to be preferred over Feed-Forward Neural Networks (FNN). First, preprocessing is described in detail including a wide overview of both calculating the relevant quantities from global navigation satellite system (GNSS) data in several steps and matching these with data from the chosen map provider. Next, an RNN including Long Short-Term Memory (LSTM) cells in an Encoder–Decoder configuration and a regular FNN are trained and applied. The models are used to forecast real driving profiles over different time horizons, both including and excluding map data in the model. Afterwards, a comparison is presented, including a quantitative and a qualitative analysis. The accuracy of the predictions is therefore assessed using Root Mean Square Error (RMSE) computations and analyses in the time domain. The results show a significant improvement in velocity prediction with LSTMs including map data. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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17 pages, 7983 KiB  
Article
Driving Robot for Reproducible Testing: A Novel Combination of Pedal and Steering Robot on a Steerable Vehicle Test Bench
by Philip Rautenberg, Clemens Kurz, Martin Gießler and Frank Gauterin
Vehicles 2022, 4(3), 727-743; https://doi.org/10.3390/vehicles4030041 - 22 Jul 2022
Cited by 5 | Viewed by 2115
Abstract
Shorter development times, increased standards for vehicle emissions and a greater number of vehicle variants result in a higher level of complexity in the vehicle development process. Efficient development of powertrain and driver assistance functions under comparable and reproducible operating conditions is possible [...] Read more.
Shorter development times, increased standards for vehicle emissions and a greater number of vehicle variants result in a higher level of complexity in the vehicle development process. Efficient development of powertrain and driver assistance functions under comparable and reproducible operating conditions is possible on vehicle test benches. Yet, the realistic simulation of real driving environments on test benches is a challenge. Current test procedures and new technologies, such as Real Driving Emission tests and Autonomous Driving, require a reproducible and even more detailed simulation of the driving environment. Due to this, the simulation of curve driving in particular is gaining in importance. This results from its significant influence on energy consumption and Autonomous Driving functions with lateral guidance, such as lane departure and evasion assistance. Reproducibility can be additionally increased by using a driving robot. At today’s vehicle test benches, pedal and shift robots are predominantly used for longitudinal dynamic tests in the performed test procedures. In order to meet these new test automation requirements for vehicle test benches, the cooperative operation of pedal and steering robots is needed on a test bench setup suitable for this purpose. In this publication, the authors present the setup of a vehicle test bench to be used in automated and reproducible vehicle-in-the-loop tests during steering events. The focus is on the test-bench-specific setup with steerable front wheels, the actuators for simulating the wheel steering torque around the steering axle and the robots used for pedals and steering wheel. Results from various test series are presented and the potential of the novel test environment is shown. The results are reproducible in various test series due to the closed-loop operation without human driving influences at the test bench. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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11 pages, 992 KiB  
Article
Understanding the Motivation and Satisfaction of Private Vehicle Users in an Eastern European Country Using Heterogeneity Analysis
by Karzan Ismael and Szabolcs Duleba
Vehicles 2022, 4(2), 409-419; https://doi.org/10.3390/vehicles4020024 - 27 Apr 2022
Cited by 5 | Viewed by 2428
Abstract
Transport service provision in many urban areas is dominated by car users, resulting in several traffic externality issues (e.g., noise, pollution, accidents). This paper investigates the perception and satisfaction of private vehicle (PV) users, including micro-mobility users, during their commute by car in [...] Read more.
Transport service provision in many urban areas is dominated by car users, resulting in several traffic externality issues (e.g., noise, pollution, accidents). This paper investigates the perception and satisfaction of private vehicle (PV) users, including micro-mobility users, during their commute by car in an Eastern European country context. The study used empirical data from a sample of 500 commuters in Budapest, Hungary, between October and November 2020. To achieve a deeper understanding of the motivation and explore the perception of PV users towards using sustainable transport services. For analysis in this study, descriptive statistics and segmentation techniques were applied. The key findings indicate that PV users can be attracted to using sustainable transport by designing the travel service quality to provide the level of service desired by customers. Moreover, the majority (73%) of PV commuters were satisfied or very satisfied with the quality attributes of the car service, assessed on a scale of 1 to 5; at the same time, PV users agreed that using public transport helps towards improving the environment and serves to reduce problems derived from traffic. In addition, various elements influence transport choice; for example, results from ordered logit models (OLMs) indicate that security, relaxation, flexibility and comfort are the main significant attributes influencing PV users’ overall satisfaction with cars. The results suggest the necessity for a segmentation technique in the analysis of travel attitudes and satisfaction aimed at reducing the frequency of existing car use to enhance sustainable transportation. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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31 pages, 3675 KiB  
Article
Motion Planning for Autonomous Vehicles Based on Sequential Optimization
by Maksym Diachuk and Said M. Easa
Vehicles 2022, 4(2), 344-374; https://doi.org/10.3390/vehicles4020021 - 12 Apr 2022
Cited by 6 | Viewed by 3122
Abstract
This study presents the development and analysis of a technique for planning the autonomous vehicle (AV) motion references using sequential optimization. The method determines the trajectory plan, speed and acceleration distributions, and other AV kinematic parameters. The approach combines the basics of the [...] Read more.
This study presents the development and analysis of a technique for planning the autonomous vehicle (AV) motion references using sequential optimization. The method determines the trajectory plan, speed and acceleration distributions, and other AV kinematic parameters. The approach combines the basics of the finite element method (FEM) and nonlinear optimization with nonlinear constraints. First, we briefly described the generalization of representing an arbitrary function by finite elements (FE) within a road segment. We chose a one-dimensional FE with two nodes and three degrees of freedom (DOF) in a node corresponding to the 5th-degree polynomial. Next, we presented a method for defining the motion trajectory. The following are considered: the formation of a restricted space for the AV’s allowable maneuvering, the motion trajectory geometry and its relation with vehicle steerability parameters, cost functions and their influences on the desirable trajectory’s nature, and the compliance of nonlinear restrictions of the node parameters with the motion area boundaries. In the second stage, we derived a technique for optimizing the AV’s speed and acceleration redistributions. The model considers possible combinations of objective functions, limiting the kinematic parameters by the tire slip critical speed, maximum speed level, maximum longitudinal acceleration, and critical lateral acceleration. In the simulation section, we compared several variants of trajectories and versions of distributing the longitudinal speed and acceleration curves. The advantages, drawbacks, and conclusions regarding the proposed technique are presented. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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12 pages, 6701 KiB  
Article
A Refined-Line-Based Method to Estimate Vanishing Points for Vision-Based Autonomous Vehicles
by Shengyao Shen, Shanshan Wang, Luping Wang and Hui Wei
Vehicles 2022, 4(2), 314-325; https://doi.org/10.3390/vehicles4020019 - 22 Mar 2022
Cited by 2 | Viewed by 1790
Abstract
Helping vehicles estimate vanishing points (VPs) in traffic environments has considerable value in the field of autonomous driving. It has multiple unaddressed issues such as refining extracted lines and removing spurious VP candidates, which suffers from low accuracy and high computational cost in [...] Read more.
Helping vehicles estimate vanishing points (VPs) in traffic environments has considerable value in the field of autonomous driving. It has multiple unaddressed issues such as refining extracted lines and removing spurious VP candidates, which suffers from low accuracy and high computational cost in a complex traffic environment. To address these two issues, we present in this study a new model to estimate VPs from a monocular camera. Lines that belong to structured configuration and orientation are refined. At that point, it is possible to estimate VPs through extracting their corresponding vanishing candidates through optimal estimation. The algorithm requires no prior training and it has better robustness to color and illumination on the base of geometric inferences. Through comparing estimated VPs to the ground truth, the percentage of pixel errors were evaluated. The results proved that the methodology is successful in estimating VPs, meeting the requirements for vision-based autonomous vehicles. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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16 pages, 4044 KiB  
Article
Autonomous Human-Vehicle Leader-Follower Control Using Deep-Learning-Driven Gesture Recognition
by Joseph Schulte, Mark Kocherovsky, Nicholas Paul, Mitchell Pleune and Chan-Jin Chung
Vehicles 2022, 4(1), 243-258; https://doi.org/10.3390/vehicles4010016 - 09 Mar 2022
Cited by 8 | Viewed by 3503
Abstract
Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems in the known literature. We present a [...] Read more.
Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems in the known literature. We present a system in which an autonomous vehicle—our ACTor 1 platform—can follow a human leader who controls the vehicle through hand-and-body gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2. Results show that our modular pipeline design reliably recognizes human body language and translates the body language into LFA commands in real time. This work has numerous applications such as material transport in industrial contexts. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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15 pages, 2545 KiB  
Article
Head Tracking in Automotive Environments for Driver Monitoring Using a Low Resolution Thermal Camera
by Christoph Weiss, Alexander Kirmas, Sören Lemcke, Stefan Böshagen, Marian Walter, Lutz Eckstein and Steffen Leonhardt
Vehicles 2022, 4(1), 219-233; https://doi.org/10.3390/vehicles4010014 - 08 Mar 2022
Cited by 2 | Viewed by 2430
Abstract
The steady enhancement of driver assistance systems and the automation of driving functions are in need of advanced driver monitoring functionalities. To evaluate the driver state, several parameters must be acquired. A basic parameter is the position of the driver, which can be [...] Read more.
The steady enhancement of driver assistance systems and the automation of driving functions are in need of advanced driver monitoring functionalities. To evaluate the driver state, several parameters must be acquired. A basic parameter is the position of the driver, which can be useful for comfort automation or medical applications. Acquiring the position through cameras can be used to provide multiple information at once. When using infrared cameras, not only the position information but also the thermal information is available. Head tracking in the infrared domain is still a challenging task. The low resolution of affordable sensors makes it especially difficult to achieve high robustness due the lack of detailed images. In this paper, we present a novel approach for robust head tracking based on template matching and optical flow. The method has been tested on various sets of subjects containing different head shapes. The evaluation does not only include the original sensor size, but also downscaled images to simulate low resolution sensors. A comparison with the ground truth is performed for X- and Y-coordinate separately for each downscaled resolution. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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17 pages, 1017 KiB  
Article
A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence
by Felix Deufel, Martin Gießler and Frank Gauterin
Vehicles 2022, 4(1), 182-198; https://doi.org/10.3390/vehicles4010012 - 01 Mar 2022
Cited by 5 | Viewed by 2382
Abstract
In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for [...] Read more.
In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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15 pages, 27693 KiB  
Article
Physics-Based Simulation and Automation of a Load-Haul-Dump Operation for an Articulated Dump Truck
by Bilal Hejase and Umit Ozguner
Vehicles 2022, 4(1), 167-181; https://doi.org/10.3390/vehicles4010011 - 22 Feb 2022
Cited by 2 | Viewed by 3554
Abstract
Many trucks are used for a class of activities involving a sequence of basic load-haul-dump operations. The repetitiveness of this operation has been an enabler for autonomous vehicle technology in efforts to increase safety and efficiency. In this paper, we present a framework [...] Read more.
Many trucks are used for a class of activities involving a sequence of basic load-haul-dump operations. The repetitiveness of this operation has been an enabler for autonomous vehicle technology in efforts to increase safety and efficiency. In this paper, we present a framework for the automation of the load-haul-dump operation in a mine setting using an articulated dump truck. A simulation environment for the testing of autonomous driving algorithms is developed and a custom mining environment is generated to adapt to our simulation settings. We also present an operational decomposition of the sequence of tasks and develop a finite state machine for high-level decision making based on this decomposition. A path tracking module that considers both bodies of the articulated truck is also developed. The resulting architecture was implemented to achieve autonomy for a load-haul-dump operation in the simulated environment within a fixed path. Experiments show that the proposed FSM-path tracking system can automate the load-haul-dump operation; and that the simulation environment can support the testing and development of autonomous driving algorithms for configurations such as an articulated truck. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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11 pages, 1345 KiB  
Article
The Effects of Wearing Helmets on Reaction of Motorcycle Riders
by Dengchuan Cai, Yu-Hsuan Chen and Chih-Jen Lee
Vehicles 2021, 3(4), 840-850; https://doi.org/10.3390/vehicles3040050 - 01 Dec 2021
Viewed by 2729
Abstract
In Taiwan, motorcycles are the most commonly used means of transportation and also have the highest accident rate. Because motorcycles are less stable and provide less protection than cars, motorcycle riders are vulnerable in traffic accidents. Furthermore, head trauma is often fatal, causing [...] Read more.
In Taiwan, motorcycles are the most commonly used means of transportation and also have the highest accident rate. Because motorcycles are less stable and provide less protection than cars, motorcycle riders are vulnerable in traffic accidents. Furthermore, head trauma is often fatal, causing a great loss to society. Although helmets provide protection to the head, they also affect the visual field of motorcycle riders. However, the literature mostly focuses on the protective effect of helmets after a collision and rarely considers the influence of helmets prior to collisions. In the study design, participants wore three different types of helmet and watched a pre-recorded video of an actual street with pre-placed warning triangles at a speed of 60 km/h. Participants were asked to press a button when they saw a warning triangle. The time between participants seeing the warning triangle and arriving at the warning triangle was calculated. This time is referred to as the “early reaction time.” The number of missed presses and false presses was also recorded. The results of the study show that: (1) Of the three types of helmet, wearing half helmets produced the longest early reaction times, followed by 3/4 helmets, with full face helmets with the shortest early reaction times. (2) Early reaction times when wearing a half helmet were the same as early reaction times when not wearing a helmet. (3) The results for the total number of missed and false presses when wearing the three types of helmet were the same as for the results of the early reaction time analysis. (4) Sex and age had no effect on early reaction times. The experimental results can be used as a reference for helmet design and academic research. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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14 pages, 2770 KiB  
Article
An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles
by Dario Niermann, Alexander Trende, Klas Ihme, Uwe Drewitz, Cornelia Hollander and Franziska Hartwich
Vehicles 2021, 3(4), 764-777; https://doi.org/10.3390/vehicles3040045 - 10 Nov 2021
Cited by 2 | Viewed by 2420
Abstract
The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling [...] Read more.
The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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30 pages, 8035 KiB  
Article
Camera-Based Lane Detection—Can Yellow Road Markings Facilitate Automated Driving in Snow?
by Ane Dalsnes Storsæter, Kelly Pitera and Edward McCormack
Vehicles 2021, 3(4), 661-690; https://doi.org/10.3390/vehicles3040040 - 13 Oct 2021
Cited by 3 | Viewed by 5656
Abstract
Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are [...] Read more.
Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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10 pages, 2738 KiB  
Article
Development and Evaluation of a Threshold-Based Motion Cueing Algorithm
by Edward Kraft, Ping He and Stephan Rinderknecht
Vehicles 2021, 3(4), 636-645; https://doi.org/10.3390/vehicles3040038 - 02 Oct 2021
Cited by 2 | Viewed by 2556
Abstract
In this paper, a motion cueing algorithm (MCA) without a frequency divider is proposed, which aims to reproduce the longitudinal reference acceleration as far as possible via tilt coordination. Using a second-order rate limit, the human perception thresholds can directly be taken into [...] Read more.
In this paper, a motion cueing algorithm (MCA) without a frequency divider is proposed, which aims to reproduce the longitudinal reference acceleration as far as possible via tilt coordination. Using a second-order rate limit, the human perception thresholds can directly be taken into account when parameterizing the MCA. The washout is compensated by tilt coordination and means of feedback from the translational acceleration. The proposed MCA is compared with the classical washout algorithm and the compensation MCA based on selected qualitative metrics and their workspace demand. In addition, a subjective study on the evaluation of the MCA was conducted. The results show that even high washout rates are not noticeable by the test subjects. Overall, the MCA was rated as very good. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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24 pages, 1809 KiB  
Article
Safety and Risk Analysis of Autonomous Vehicles Using Computer Vision and Neural Networks
by Aditya Dixit, Ramesh Kumar Chidambaram and Zaheer Allam
Vehicles 2021, 3(3), 595-617; https://doi.org/10.3390/vehicles3030036 - 15 Sep 2021
Cited by 9 | Viewed by 7636
Abstract
The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to [...] Read more.
The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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12 pages, 4226 KiB  
Article
An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
by Lorenzo Scappaticci, Giacomo Risitano, Dario Santonocito, Danilo D’Andrea and Dario Milone
Vehicles 2021, 3(3), 545-556; https://doi.org/10.3390/vehicles3030033 - 23 Aug 2021
Cited by 1 | Viewed by 3644
Abstract
The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign [...] Read more.
The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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13 pages, 7663 KiB  
Article
A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider
by Francesco Carputo, Danilo D’Andrea, Giacomo Risitano, Aleksandr Sakhnevych, Dario Santonocito and Flavio Farroni
Vehicles 2021, 3(3), 377-389; https://doi.org/10.3390/vehicles3030023 - 15 Jul 2021
Cited by 9 | Viewed by 2958
Abstract
A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of [...] Read more.
A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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20 pages, 6371 KiB  
Article
On the Tripped Rollovers and Lateral Skid in Three-Wheeled Vehicles and Their Mitigation
by Martín-Antonio Rodríguez-Licea
Vehicles 2021, 3(3), 357-376; https://doi.org/10.3390/vehicles3030022 - 11 Jul 2021
Cited by 3 | Viewed by 2306
Abstract
Active safety systems for three-wheeled vehicles seem to be in premature development; in particular, delta types, also known as tuk-tuks or sidecars, are sold with minimal protection against accidents. Unfortunately, the risk of wheel lifting and lateral and/or longitudinal vehicle roll is high. [...] Read more.
Active safety systems for three-wheeled vehicles seem to be in premature development; in particular, delta types, also known as tuk-tuks or sidecars, are sold with minimal protection against accidents. Unfortunately, the risk of wheel lifting and lateral and/or longitudinal vehicle roll is high. For instance, a tripped rollover occurs when a vehicle slides sideways, digging its tires into soft soil or striking an object. Unfortunately, research is mostly aimed at un-tripped rollovers while most of the rollovers are tripped. In this paper, models for lateral skid tripped and un-tripped rollover risks are presented. Later, independent braking and accelerating control actions are used to develop a dynamic stability control (DSC) to assist the driver in mitigating such risks, including holes/bumps road-scenarios. A common Lyapunov function and an LMI problem resolution ensure robust stability while optimization allows tuning the controller. Numerical and HIL tests are presented. Implementation on a three-wheeled vehicle requires an inertial measurement unit, and independent ABS and propulsion control as main components. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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Review

Jump to: Research

22 pages, 4814 KiB  
Review
Exploring Smart Tires as a Tool to Assist Safe Driving and Monitor Tire–Road Friction
by Maria Pomoni
Vehicles 2022, 4(3), 744-765; https://doi.org/10.3390/vehicles4030042 - 26 Jul 2022
Cited by 17 | Viewed by 4603
Abstract
Road surface friction, or in other words, a pavement’s skid resistance, is an essential attribute of highway safety, acting as a liaison between the infrastructure condition and the driver’s response to it through proper vehicle maneuvering. The present study reviews aspects related to [...] Read more.
Road surface friction, or in other words, a pavement’s skid resistance, is an essential attribute of highway safety, acting as a liaison between the infrastructure condition and the driver’s response to it through proper vehicle maneuvering. The present study reviews aspects related to the tire–road friction, including affecting factors, monitoring systems and related practices, and demonstrates the efficacy of using smart tires, or tires embedded with sensors, for the purpose of evaluating roadway friction levels in real-time while traveling. Such an approach is expected to assist drivers in adjusting their behavior (i.e., lowering their speed) in the event that signs of reduced skid resistance are observed in favor of road safety. The current challenges and research prospects are highlighted in terms of tire manufacturers’ perspectives as well as future mobility patterns with autonomous driving modes. Overall, smart tires are commented as a tool able to enhance drivers’ safety for both current and future mobility patterns, help to control pavement deterioration and complement existing practices for infrastructure condition assessment. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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