scholarly journals Safety of Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jun Wang ◽  
Li Zhang ◽  
Yanjun Huang ◽  
Jian Zhao ◽  
Francesco Bella
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Statistical Investigation ◽  
The Other ◽  
Automated System ◽  
Future Research ◽  
Practical Applications ◽  
Safety Risks ◽  
Road Testing

Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10−4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.

scholarly journals On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6733
Author(s):  
Min-Joong Kim ◽  
Sung-Hun Yu ◽  
Tong-Hyun Kim ◽  
Joo-Uk Kim ◽  
Young-Min Kim
Keyword(s):  
Autonomous Vehicles ◽  
Focal Length ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Radar Sensors ◽  
Safety And Reliability ◽  
Time Required ◽  
Variable Focus ◽  
Camera Sensors

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

scholarly journals Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

2020 ◽  
Vol 10 (18) ◽  
pp. 6317 ◽  
Author(s):  
Wilfried Wöber ◽  
Georg Novotny ◽  
Lars Mehnen ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Parameter Estimation ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
The Other ◽  
Sensor Data ◽  
Critical Feature ◽  
Motion Data ◽  
Movement Data ◽  
End Use ◽  
Vehicle Movement

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.

scholarly journals The Impact of Social Influence, Technophobia, and Perceived Safety on Autonomous Vehicle Technology Adoption

2019 ◽  
Vol 48 (2) ◽  
pp. 133-142
Author(s):  
Sahil Koul ◽  
Ali Eydgahi
Keyword(s):  
Technology Adoption ◽  
Social Influence ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Negative Relationship ◽  
Future Research ◽  
Intention To Use ◽  
Perceived Safety ◽  
Vehicle Technology ◽  
The Impact

The objective of this study was to determine whether there was a relationship between social influence, technophobia, perceived safety of autonomous vehicle technology, number of automobile-related accidents and the intention to use autonomous vehicles. The methodology was a descriptive, cross-sectional, correlational study. Theory of Planned Behavior provided the underlying theoretical framework. An online survey was the primary method of data collection. Pearson’s correlation and multiple linear regression were used for data analysis. This study found that both social influence and perceived safety of autonomous vehicle technology had significant, positive relationships with the intention to use autonomous vehicles. Additionally, a significant negative relationship was found among technophobia and intention to use autonomous vehicles. However, no relationship was found between the number of automobile-related accidents and intention to use autonomous vehicles. This study presents several original and significant findings as a contribution to the literature on autonomous vehicle technology adoption and proposes new dimensions of future research within this emerging field.

Virtual Simulations Environment Development for Autonomous Vehicles Interaction

2020 ◽  
Author(s):  
Mohsen Malayjerdi ◽  
Vladimir Kuts ◽  
Raivo Sell ◽  
Tauno Otto ◽  
Barış Cem Baykara
Keyword(s):  
Virtual Environment ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Mapping Method ◽  
Human Robot Interaction ◽  
Industrial Robots ◽  
Simulation Environment ◽  
Road Users ◽  
Virtual Simulations ◽  
Road Testing

Abstract One of the primary verification criteria of the autonomous vehicle is safe interaction with other road users. Based on studies, real-road testing is not practical for safety validation due to its time and cost consuming. Therefore, simulating miles driven is the only feasible way to overcome this limitation. The primary goal of the related research project is to develop advanced techniques in the human-robot interaction (HRI) safety validation area by usage of immersive simulation technologies. Developing methods for the creation of the simulation environment will enable us to do experiments in a digital environment rather than real. The main aim of the paper is to develop an effective method of creating a virtual environment for performing simulations on industrial robots, mobile robots, and autonomous vehicles (AGV-s) from the safety perspective for humans. A mid-size drone was used for aerial imagery as the first step in creating a virtual environment. Then all the photos were processed in several steps to build the final 3D map. Next, this mapping method was used to create a high detail simulation environment for testing an autonomous shuttle. Developing efficient methods for mapping real environments and simulating their variables is crucial for the testing and development of control algorithms of autonomous vehicles.

scholarly journals Autonomous Driving—A Crash Explained in Detail

2019 ◽  
Vol 9 (23) ◽  
pp. 5126 ◽  
Author(s):  
Betz ◽  
Heilmeier ◽  
Wischnewski ◽  
Stahl ◽  
Lienkamp
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Software Test ◽  
Planning And Control ◽  
Modular Software ◽  
Fatal Crash ◽  
Detailed Explanation ◽  
A Minor ◽  
And Control

Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstancescausing the crash are elaborated in detail. By presented and explaining in detail which softwarepart failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous.

From Neurorobotic Localization to Autonomous Vehicles

2019 ◽  
Vol 07 (03) ◽  
pp. 183-194
Author(s):  
Yoan Espada ◽  
Nicolas Cuperlier ◽  
Guillaume Bresson ◽  
Olivier Romain
Keyword(s):  
Visual Information ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Real Data ◽  
Autonomous Driving ◽  
Recognition System ◽  
Indoor Navigation ◽  
Learning System ◽  
Place Recognition ◽  
Outdoor Environments

The navigation of autonomous vehicles is confronted to the problem of an efficient place recognition system which is able to handle outdoor environments on the long run. The current Simultaneous Localization and Mapping (SLAM) and place recognition solutions have limitations that prevent them from achieving the performances needed for autonomous driving. This paper suggests handling the problem from another perspective by taking inspiration from biological models. We propose a neural architecture for the localization of an autonomous vehicle based on a neurorobotic model of the place cells (PC) found in the hippocampus of mammals. This model is based on an attentional mechanism and only takes into account visual information from a mono-camera and the orientation information to self-localize. It has the advantage to work with low resolution camera without the need of calibration. It also does not need a long learning phase as it uses a one-shot learning system. Such a localization model has already been integrated in a robot control architecture which allows for successful navigation both in indoor and small outdoor environments. The contribution of this paper is to study how it passes the scale change by evaluating the performance of this model over much larger outdoor environments. Eight experiments using real data (image and orientation) grabbed by a moving vehicle are studied (coming from the KITTI odometry datasets and datasets taken with VEDECOM vehicles). Results show the strong adaptability to different kinds of environments of this bio-inspired model primarily developed for indoor navigation.

scholarly journals Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

2021 ◽  
Author(s):  
De Jong Yeong ◽  
Gustavo Velasco-Hernandez ◽  
John Barry ◽  
Joseph Walsh
Keyword(s):  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Future Research ◽  
Software Systems ◽  
Radar Sensors ◽  
Technical Performance ◽  
Driving Task ◽  
Single Sensor

The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

scholarly journals Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Ze Liu ◽  
Yingfeng Cai ◽  
Hai Wang ◽  
Long Chen
Keyword(s):  
High Precision ◽  
Autonomous Vehicles ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
Environmental Sensors ◽  
Detection And Tracking ◽  
Vehicle Test ◽  
Objects Detection

AbstractRadar and LiDAR are two environmental sensors commonly used in autonomous vehicles, Lidars are accurate in determining objects’ positions but significantly less accurate as Radars on measuring their velocities. However, Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. In order to compensate for the low detection accuracy, incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR, in this paper, an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles. By employing the Unscented Kalman Filter, Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle. Finally, the real vehicle test under various driving environment scenarios is carried out. The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy. Compared with a single sensor, it has obvious advantages and can improve the intelligence level of autonomous cars.

Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S

2017 ◽  
Vol 11 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Mica R. Endsley
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Model Development ◽  
Autonomous Driving ◽  
Discrete Control ◽  
Driving Safety ◽  
Future Research ◽  
Continuous Control ◽  
Development Mode

Autonomous and semiautonomous vehicles are currently being developed by over14 companies. These vehicles may improve driving safety and convenience, or they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. I conducted a naturalistic driving study on the autonomy features in the Tesla Model S, recording my experiences over a 6-month period, including assessments of SA and problems with the autonomy. This preliminary analysis provides insights into the challenges that drivers may face in dealing with new autonomous automobiles in realistic driving conditions, and it extends previous research on human-autonomy interaction to the driving domain. Issues were found with driver training, mental model development, mode confusion, unexpected mode interactions, SA, and susceptibility to distraction. New insights into challenges with semiautonomous driving systems include increased variability in SA, the replacement of continuous control with serial discrete control, and the need for more complex decisions. Issues that deserve consideration in future research and a set of guidelines for driver interfaces of autonomous systems are presented and used to create recommendations for improving driver SA when interacting with autonomous vehicles.

scholarly journals How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting

2018 ◽  
Vol 2672 (46) ◽  
pp. 11-20 ◽  
Author(s):  
Felix Steck ◽  
Viktoriya Kolarova ◽  
Francisco Bahamonde-Birke ◽  
Stefan Trommer ◽  
Barbara Lenz
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Future Research ◽  
Related Factors ◽  
Travel Time Savings ◽  
Time Savings ◽  
The Impact ◽  
Privately Owned

Autonomous driving is being discussed as a promising solution for transportation-related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent traveling as they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and little empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using stated-choice experiments. Two use cases were addressed – a privately owned, and a shared autonomous vehicle – compared with other modes of transportation. The collected data were analyzed by performing a mixed logit model. The results show that mode-related factors such as time elements, especially in-vehicle time and cost, play a crucial role for mode choices that include autonomous vehicles. The study provides empirical evidence that autonomous driving may lead to a reduction in VTTS for commuting trips. It was found that driving autonomously in a privately owned vehicle might reduce the VTTS by 31% compared with driving manually, and is perceived similarly to in-vehicle time in public transportation. Furthermore, riding in a shared autonomous vehicle is perceived 10% less negatively than driving manually. The study provides important insights into VTTS by autonomous driving for commuting trips and could be a base for future research to build upon.

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