Vehicle Safety and Automated Driving

With increasing automation in the rail sector, the train driver’s task changes from full control to a supervisory position. This bears the risk of monotony and subsequent changes in visual attention, possibly for the worse. Similar to concepts in car driving, one solution for this could be driver state monitoring with triggered interventions in case of declining task attention. Previous research on train drivers’ visual attention has used eye tracking. In contrast, head tracking is easier to realize within the train driver cabin. This study set out to test whether head tracking is a feasible alternative to eye tracking and can provide similar findings. Based on previous eye-tracking research, we compared differences in head movements in automated vs. manual driving, and for different levels of driving speed and driving experience. We conducted a study with 25 active train drivers in a high-fidelity train simulator. Statistical analyses revealed no significant difference in the vertical head movements between automation levels. There was a significant difference in the horizontal head movements, with train drivers looking more to the right for manual driving. We found no significant influence of driving speed and experience on head movements. Safety implications and the feasibility of head tracking as an alternative to eye tracking are discussed. Full article

A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, despite its few demerits. Sufficient progress in accuracy and depth computation speed has manifested during the period. Over the years, stereo depth estimation has been provided with various training modes, such as supervised, self-supervised, and unsupervised, before deploying it for real-time performance. These modes are to be used depending on the application and/or the availability of datasets for training. Deep learning, on the other hand, has provided the stereo depth estimation methods with a new life to breathe in the form of enhanced accuracy and quality of images, attempting to successfully reduce the residual errors in stages in some of the methods. Furthermore, depth estimation from a single RGB image has been intricate since it is an ill-posed problem with a lack of geometric constraints and ambiguities. However, this monocular depth estimation has gained popularity in recent years due to the development in the field, with appreciable improvements in the accuracy of depth maps and optimization of computational time. The help is mostly due to the usage of CNNs (Convolutional Neural Networks) and other deep learning methods, which help augment the feature-extraction phenomenon for the process and enhance the quality of depth maps/accuracy of MDE (monocular depth estimation). Monocular depth estimation has seen improvements in many algorithms that can be deployed to give depth maps with better clarity and details around the edges and fine boundaries, which thus helps in delineating between thin structures. This paper reviews various recent deep learning-based stereo and monocular depth prediction techniques emphasizing the successes achieved so far, the challenges acquainted with them, and those that can be expected shortly. Full article

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper. Full article

Unmanned Aerial Vehicles (UAVs) offer a promising solution for road accident scene documentation. This study seeks to investigate the occurrence of systematic deformations, such as bowling and doming, in the 3D point cloud and orthomosaic generated from images captured by UAVs along an horizontal road segment, while exploring how adjustments in flight patterns can rectify these errors. Four consumer-grade UAVs were deployed, all flying at an altitude of 10 m while acquiring images along two different routes. Processing solely nadir images resulted in significant deformations in the outputs. However, when additional images from a circular flight around a designated Point of Interest (POI), captured with an oblique camera axis, were incorporated into the dataset, these errors were notably reduced. The resulting measurement errors remained within the 0–5 cm range, well below the customary error margins in accident reconstruction. Remarkably, the entire procedure was completed within 15 min, which is half the estimated minimum duration for scene investigation. This approach demonstrates the potential for UAVs to efficiently record road accident sites for official documentation, obviating the need for pre-established Ground Control Points (GCP) or the adoption of Real-Time Kinematic (RTK) drones or Post Processed Kinematic (PPK) technology. Full article

Veterans face difficulties accessing vital health and community services, especially in rural areas. Autonomous vehicles (AVs) can revolutionize transportation by enhancing access, safety, and efficiency. Yet, there is limited knowledge about how Veterans perceive AVs. This study fills this gap by assessing Veterans’ AV perceptions before and after exposure to an autonomous shuttle (AS). Using a multi-method approach, 23 participants completed pre- and post-AS Autonomous Vehicle User Perception Survey (AVUPS), with 10 participants also taking part in post-AS focus groups. Following exposure to the AS, differences were observed for three out of the four AVUPS domains: an increase in Intention to Use (p < 0.01), a decrease in Perceived Barriers (p < 0.05), and an increase in Total Acceptance (p = 0.01); Well-being remained unchanged (p = 0.81). Feedback from focus groups uncovered six qualitative themes: Perceived Benefits (n = 70), Safety (n = 66), Shuttle Experience (n = 47), AV Adoption (n = 44), Experience with AVs (n = 17), and Perception Change (n = 10). This study underscores AVs’ potential to alleviate transportation challenges faced by Veterans, contributing to more inclusive transportation solutions. The research offers insights for future policies and interventions aimed at integrating AV technology into the transportation system, particularly for mobility-vulnerable Veterans in rural and urban settings. Full article

Path planning is the most fundamental necessity for autonomous mobile robots. Traditionally, the path planning problem was solved using analytical methods, but these methods need perfect localization in the environment, a fully developed map to plan the path, and cannot deal with complex environments and emergencies. Recently, deep neural networks have been applied to solve this complex problem. This review paper discusses path-planning methods that use neural networks, including deep reinforcement learning, and its different types, such as model-free and model-based, Q-value function-based, policy-based, and actor-critic-based methods. Additionally, a dedicated section delves into the nuances and methods of robot interactions with pedestrians, exploring these dynamics in diverse environments such as sidewalks, road crossings, and indoor spaces, underscoring the importance of social compliance in robot navigation. In the end, the common challenges faced by these methods and applied solutions such as reward shaping, transfer learning, parallel simulations, etc. to optimize the solutions are discussed. Full article

The vast majority of autonomous driving systems are limited to applications on roads with clear lane markings and are implemented using commercial-grade sensing systems coupled with specialized graphic accelerator hardware. This research reviews an alternative approach for autonomously steering vehicles that eliminates the dependency on road markings and specialized hardware. A combination of machine vision, machine learning, and artificial intelligence based on popular pre-trained Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the Autonomous Campus Transport (ACTor) vehicle—an autonomous vehicle development and research platform coupled with a low-cost webcam-based sensing system and minimal computational resources. The proposed solution was evaluated on real-world roads and varying environmental conditions. It was found that this solution may be used to successfully navigate unmarked roads autonomously with acceptable road-following behavior. Full article

Camera-based mirror systems (CBMSs) are a relatively new technology in the automotive industry, and much of the United States’ medium- and heavy-duty commercial fleet has been reluctant to convert from standard glass, or “west coast”, mirrors to CBMSs. CBMSs have the potential to reduce the number of truck and passenger vehicle incidents, improving overall fleet safety. CBMSs also have the potential to improve operational efficiency by improving aerodynamics and reducing drag, resulting in better fuel economy, and improving maneuverability. Improvements in overall safety are also possible; the field of view for the driver is potentially 360° with the addition of trailer cameras, allowing for visibility of the rear of the trailer and the front of the truck. These potential improvements seem promising, but the literature on driver surveys clearly shows that there is reluctance to adopt this technology for many reasons. Additionally, more robust testing in the laboratory and in the field is necessary to determine whether CBMSs are adequate to replace standard mirrors on trucks. This analysis provides an overview of key research questions for CBMS testing based on the current literature on the topic (surveys, standards, and previous testing). The purpose of this analysis is to serve as guidance in developing further testing of CBMSs, especially testing involving human subjects. Full article

The positional deviation of the in-vehicle Automatic Emergency Call System (AECS) under collision conditions brings difficulties for Intelligent Connected Vehicles (ICVs) post rescue operations. Currently, there is a lack of analysis on system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in the AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on Signal-Based In-Vehicle Emergency Call Systems. We also propose environmental limits applicable to comprehensive tests, which can objectively evaluate reliability and provide data support for the AECS bench test through a satellite-signal-based semi-physical simulation, which is subjected to a bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibration and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4 gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature difference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of the satellite signal simulation, necessary environmental conditions for the test are obtained, including a temperature that is consistent with the maximum operating temperature of the vector generator and a vibration power spectral density (PSD) lower than 1.2 gRMS. Full article

Our paper introduces new reconstruction techniques of real-life critical road traffic accidents focusing on highly automated functions. The investigation method presented here focuses on the effect of relevant control parameters and environmental factors following the concept of sensitivity analysis. Two reconstruction tools are applied, the choice depending on the relevant causal factor of the accidents. Our measurement proves that the technical parameters of the control process, like time to collision or braking pressure that affects user satisfaction directly, can significantly influence the probability of accident occurrence. Thus, it is reasonable to consider safety with an increased weight compared to the user experience when identifying these parameters’ values. On the other hand, the effects of the investigated environmental factors were also found to be significant. Accordingly, future ADAS applications need to consider the change of environmental factors in the case of increased risk level, and driver-mode should be adapted to the new situation. Full article

Due to an increase in population, urban centers are currently seeing an increase in traffic, resulting in negative consequences such as pollution and congestion. Efforts have been made to promote a modal shift towards the use of more sustainable means of transport, such as walking and cycling, but several deterrents influence the citizens’ perceptions of safety, security and comfort, discouraging their choice of active modes of transport. This study focuses on the importance of providing meaningful information to vulnerable road users (VRUs) to support their perceptions and objectives while moving within urban spaces through a novel concept of route planning. A broad survey of the needs and concerns of VRUs through interviews, focus groups and questionnaires, applied to the Portuguese population of the Metropolitan Area of Porto, led to the development of a new concept of route planners that show personalized routes according to the individual perceptions of each user. This concept is materialized in a route planner prototype that has been extensively tested by potential users. Subjective evaluation and feedback showed the usefulness of the concept and added value to a familiar product, leading to a satisfying experience for participants. This study shows that there is an opportunity to improve these tools to provide a higher degree of power and customization to users on route planning, which includes addressing mobility restrictions and personal perceptions of safety, security and comfort. The ultimate goal of this new approach is to persuade citizens to switch to more sustainable means of transport. Full article

In this work, a simulation framework for virtual testing of autonomous driving functions under the influence of a fault occurring in a component is presented. The models consist of trajectory planning, motion control, models of actuator management, actuators and vehicle dynamics. Fault-handling tests in a right-turn maneuver are described, subject to an injected fault in the steering system. Different scenarios are discussed without and with a fault and without and with counteractions against the fault. The results of five scenarios for different criticality metrics are discussed. In the case of a fault without a counteraction, a pronounced lateral position deviation of the ego vehicle from the reference curve is observed. Furthermore, the minimal and hence most critical time-to-collision (TTC) and post-encroachment time (PET) values are calculated for each scenario together with a parameter variation of the initial position of a traffic agent. The minimum TTC values are lowest in the case of a fault without counteraction. For the lateral position deviation and the TTC, the counteractions cause reduced criticality that can become even lower than in the case without a fault, corresponding to a decrease in the dynamic behavior of the vehicle. For the PET, only in the case of a fault without counteraction, a non-zero value can be calculated. With the implemented testing toolchain, the automated vehicle and the reaction of the HAD function in non-standard conditions with reduced performance can be investigated. This can be used to test the influence of component faults on automated driving functions and help increase acceptance of implemented counteractions as part of the HAD function. The assessment of the situation using a combination of metrics is shown to be useful, as the different metrics can become critical in different situations. Full article

The problem of large calculation models in bus–two-wheeled vehicle traffic accidents (TA) leads to the difficulty of balancing the calculation efficiency and accuracy, as well as difficulties in accident reconstruction. Herein, two typical accidents were reconstructed, based on the rigid–flexible coupled human model (HM) and the Facet vehicle model, and the vehicle damage conditions and the human head biomechanical injury were analyzed. The simulation results showed that the physical process of the human–vehicle collision was basically consistent with the accident video, the windshield fracture was consistent with the actual vehicle report, and the human biomechanical injury characteristics were also consistent with the autopsy report, which verified the feasibility of the simulation model, and provides a basis and reference for forensic identification and for traffic police to deal with accident disputes. Full article

Highly autonomous vehicles (HAV) have the potential of improving road safety and providing alternative transportation options. Given the novelty of HAVs, high-fidelity driving simulators operating in an autonomous mode are a great way to expose transportation users to HAV prior to HAV adoption. In order to avoid the undesirable effects of simulator sickness, it is important to examine whether factors such as age, sex, visual processing speed, and exposure to acclimation scenario predict simulator sickness in driving simulator experiments designed to replicate the HAV experience. This study identified predictors of simulator sickness provocation across the lifespan (N = 210). Multiple stepwise backward regressions identified that slower visual processing speed predicts the Nausea and Dizziness domain with age not predicting any domains. Neither sex, nor exposure to an acclimation scenario predicted any of the four domains of simulator sickness provocation, namely Queasiness, Nausea, Dizziness, and Sweatiness. No attrition occurred in the study due to simulator sickness and thus the study suggests that high-fidelity driving simulator may be a viable way to introduce drivers across the lifespan to HAV, a strategy that may enhance future HAV acceptance and adoption. Full article

Introduction: Single-track vehicles (including, among others, scooters, bicycles, mopeds, and motorcycles) are becoming increasingly popular means of transport, especially in large cities. A significant disadvantage of single-track vehicles is the low level of protection of users’ bodies during road accidents, which causes life-threatening injuries. The aim of this study is to characterize the injuries of users of single-track vehicles. Material and methods: An analysis of medical documentation of the ambulance service in the region of central Poland covered cases in 2019–2020. Out of 17,446 interventions, a group of 248 road incidents involving single-track vehicles was selected. The data included the scene of the event, the sociodemographic data of the casualties, the injuries suffered, and the clinical diagnoses. Analyses of the correlation of variables with the chi-squared and Spearman’s Rho tests were applied. All results were considered significant at p < 0.05. Results: In the analyzed period, trips of men accounted for 83.5% of all of the interventions (n = 207), while trips of women accounted for 16.5% (n = 41). The mean age of the victims was 45.66 years (SD ± 20.45). Taking into account the division of single-track vehicles, individual cases were recorded with the participation of bicycles (n = 183), motorcycles (n = 61), and scooters (n = 4). Taking into account the type of event, the following were distinguished: deductions (n = 62), falls (n = 179), and sickness (n = 7). The most common injuries were to the heads of cyclists (n = 101, which constitutes 55.19% of all injuries), lower limb injuries in motorcyclists (n = 35; 57.38%), and head injuries in scooter users (n = 3; 75%). The locations of sustained injuries significantly correlated with the type of vehicle in the cases of head injuries (p = 0.046), spine/back injuries (p = 0.001), pelvis injuries (p = 0.021), and lower limb injuries (p = 0.001). Conclusions: The users of single-track vehicles injured in road accidents were more often men than women. The characteristics of the injuries depended on the type of vehicle. The lack of adequate body protection significantly increases the likelihood of death or damage to health. It is advisable to promote safety rules among users of single-track vehicles, with a particular emphasis on the protection of individual parts of the body. Full article