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Sensors and Systems for Automotive and Road Safety (Volume 2)

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5297

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Prof. Dr. Marek Jaśkiewicz

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Guest Editor

Department of Automotive Engineering and Transport, Faculty of Mechatronics and Mechanical Engineering, Kielce University of Technology, Avenue Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland
Interests: transportation; technical diagnostics; vehicle safety; accidents; biomechanics of collisions mechanics of motion
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Prof. Dr. Milos Poliak

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Guest Editor

Faculty of Operation and Economics of Transport and Communications, University of Zilina, Avenue Univerzitná 8215/1, 010 26 Žilina, Slovakia
Interests: goods distribution; public transport; road safety; intelligent transport infrastructures; automotive engineering; biomechanics
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Prof. Dr. Ondrej Stopka

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Department of Transport and Logistics, Institute of Technology and Business in České Budějovice, Avenue Okružní 517/10, 370 01 České Budějovice, Czech Republic
Interests: transport operation; city logistics; operations research; handling equipment optimization; telematics and smart technologies in transport; intelligent transport infrastructures; autonomous vehicles; road transport safety; emission research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on vehicle safety research, with an emphasis on the design, construction and equipment of vehicles with the goal of minimising the occurrence and consequences of road crashes. New developments in vehicles are intended to reduce the number of accidents caused by human error and prevent fatalities, as well as reduce fuel consumption and lower exhaust emissions. Advanced safety systems used for this purpose require sensors both inside and outside the vehicles to detect and identify objects, determine their movement parameters, monitor the behaviour of drivers and passengers and predict future behaviour in order to avoid potential collisions.

Transport and road infrastructure safety is closely related to vehicle safety. Intelligent transport infrastructure systems comprise various technologies, including telecommunications, information technology, and automatic systems using complex measurement systems and sophisticated analysis methods. These support the transport system, increasing its efficiency and improving the safety of road users.

This Special Issue intends to bring together original theoretical and empirical articles addressing safety issues in vehicles and intelligent transport infrastructure systems. Topics relevant to this Special Issue include the following: the use of different types of sensors in research to improve vehicle safety; technology, methods and sensors used in crash tests; the application of sensors to detect and identify obstacles, oncoming objects, determine their movement parameters, position and predict their future behaviour in order to avoid potential collisions; measuring methods and sensors used in vehicle traction tests and vehicle diagnostics; the use of sensors to assess the performance indicators of reciprocating internal combustion engines taking into account their harmful effects on the environment; sensors for monitoring the behaviour and psycho-physical state of drivers and passengers (e.g., visual sensors, motion sensors, breathalysers), assessment algorithms; systems and sensors for intelligent transport infrastructures to monitor road users, to perform predictive analytics and to improve traffic flow and road safety; and cargo securing systems in road transport.

Prof. Dr. Marek Jaśkiewicz
Prof. Dr. Milos Poliak
Prof. Dr. Ondrej Stopka
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

accident
accident analysis
accident prevention
accident reconstruction
artificial intelligence in automate vehicles
autonomous and connected vehicles
autonomous vehicles
big data analysis in vehicular systems
networks connected vehicles in urban roads
crash test
crashworthiness
driver behaviour monitoring
driving simulators
dummy emerging IoT applications in vehicular social networks (VSNs)
fault-tolerant systems
ground vehicle safety
intelligent transportation systems
intelligent vehicles
Internet of Vehicles
lighting of vehicles and roads
road safety
infrastructure safety of electric/hybrid cars
sensors for fault detection of vehicles
sensors for vehicle movement
smart cities
smart mobility and sustainable transport services
traffic control systems
traffic monitoring
traffic organization
traffic safety
vehicle active safety
vehicle communications: V2X, V2V, V2I
vehicle detection
vehicle dynamics
vehicle localization system
vehicle passive safety
vehicle privacy and trust
vehicle stability and handling vehicle testing
vehicle to everything
vehicular ad hoc networks (VANETs)
vehicular networks
vehicular social networks (VSNs)
visibility (recognizability) of pedestrians and obstacles
wireless in-car networks

Related Special Issue

Sensors and Systems for Automotive and Road Safety in Sensors (26 articles)

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Research

15 pages, 5790 KiB

Open AccessArticle

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles

by Siwoo Jeong, Jonghyeon Ko, Sukki Lee, Jihoon Kang, Yeni Kim, Soon Yong Park and Sungchul Mun

Sensors 2024, 24(8), 2505; https://doi.org/10.3390/s24082505 - 13 Apr 2024

Viewed by 466

Abstract

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles. Full article

(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety (Volume 2))

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22 pages, 5800 KiB

Open AccessArticle

Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction

by Thomas König, Daniel Paula, Stefan Quaschner and Hans-Georg Schweiger

Sensors 2024, 24(5), 1517; https://doi.org/10.3390/s24051517 - 26 Feb 2024

Viewed by 482

Abstract

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal, the accident can be avoided or not by the driver when reacting to this signal. Full article

(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety (Volume 2))

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19 pages, 1426 KiB

Open AccessArticle

Implementation of Automated Guided Vehicles for the Automation of Selected Processes and Elimination of Collisions between Handling Equipment and Humans in the Warehouse

by Iveta Kubasakova, Jaroslava Kubanova, Dominik Benco and Dominika Kadlecová

Sensors 2024, 24(3), 1029; https://doi.org/10.3390/s24031029 - 05 Feb 2024

Cited by 1 | Viewed by 1329

Abstract

This article deals with the implementation of automated guided vehicles (AGVs) in a selected company. The aim is to analyse the use of AGVs in our country and abroad and to provide information about the use of AGVs in other countries and operations other than ours. The result of the analysis was a literature review, which points out the individual advantages and disadvantages of the use of AGVs in companies. Within the review we also address the issue of AMR vehicles, due to the modernization of existing AGVs in the company, or the replacement of AMRs with AGVs in general. Our aim is to show why AGVs can replace human work. This is mainly because of the continuous increase in the wages of employees, because of safety, but also because of the modernization of the selected company. The company has positive experience of AGVs in other sites. We wanted to point out a higher form of automation, and how it would be possible to use AMR vehicles for the same work as AGVs. In the company, we have identified jobs where we would like to introduce AGVs or AMR vehicles. Consequently, we chose the AGV from CEIT operated by magnetic tape and the AMR from SEER as an example. Based on studies, the demand for AGVs is expected to increase by up to 17% in 2019–2024. Therefore, the company is looking into the issue of the implementation of AGVs at multiple sites. The question which remains is the economic return and the possibility of investing in the automation of processes in the company, which we discuss in more detail in the conclusion of the article and in the research. The article describes the exact processes for AGVs, their workload, and also the routes for AGVs, such as loading/unloading points, stopping points, checkpoints, junctions with other AGVs, charging stations, and field elements, as well as their speed, frequency and the possibility of collision with other AGVs. Our research shows that by applying the new technology, the company will save a large amount of money on employee wages. The purchase of two AGVs will cost the company EUR 49,000, while the original technology used in the company cost EUR 79,200 annually. The payback period for such an investment is 8 months. The benefits of implementing AGVs are evaluated in the last section of this paper, where both the economic and time requirements of the different proposals are included. This section also includes recommendations for improving specific parts of the enterprise. Full article

(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety (Volume 2))

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25 pages, 6961 KiB

Open AccessArticle

Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior

by Daniel Paula, Maximilian Bauder, Claus Pfeilschifter, Franziska Petermeier, Tibor Kubjatko, Klaus Böhm, Andreas Riener and Hans-Georg Schweiger

Sensors 2023, 23(24), 9785; https://doi.org/10.3390/s23249785 - 12 Dec 2023

Viewed by 740

Abstract

Partially automated driving functions (SAE Level 2) can control a vehicle’s longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver’s ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered. Full article

(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety (Volume 2))

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15 pages, 10576 KiB

Open AccessArticle

Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement

by Juraj Jagelcak and Jaroslava Kubanova

Sensors 2023, 23(23), 9478; https://doi.org/10.3390/s23239478 - 28 Nov 2023

Viewed by 828

Abstract

The use of sensors in monitoring lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics of delivery van for cargo securing, which is essential to the decision of where accelerometer sensors should be placed when monitoring accelerations or performing cargo securing tests. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle and load. Manual labeling of acceleration events has been used to identify different lateral acceleration events and regression analysis to determine the relationship between lateral accelerations at different sensor positions. The level of acceleration on the roof of the vehicle was found to be like that occurring on a lashed load with limited movements. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. The values of the mean lateral accelerations of the delivery van from the sensor on the roof of the vehicle are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height. Full article

(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety (Volume 2))

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19 pages, 25992 KiB

Open AccessArticle

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments

by Tae-Hyeong Kim, Gi-Hwan Jo, Hyeong-Seok Yun, Kyung-Su Yun and Tae-Hyoung Park

Sensors 2023, 23(21), 8808; https://doi.org/10.3390/s23218808 - 29 Oct 2023

Cited by 1 | Viewed by 884

Abstract

Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor’s coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach. Full article

(This article belongs to the Special Issue Sensors and Systems for Automotive and Road Safety (Volume 2))

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