Autonomous Driving Safety Against Hazardous Drivers

2021 ◽  
Author(s):  
Soumen Bhowmick
Keyword(s):  
Autonomous Driving ◽  
Driving Safety

scholarly journals Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7197
Author(s):  
Bing Lu ◽  
Hongwen He ◽  
Huilong Yu ◽  
Hong Wang ◽  
Guofa Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Attractive Potential ◽  
Vehicle Stability ◽  
Path Generation ◽  
Sigmoid Curve ◽  
Minimum Potential ◽  
Multiple Obstacles

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.

Human-vehicle dynamic model with driver’s neuromuscular characteristic for shared control of autonomous vehicle

2020 ◽  
pp. 095440702097710
Author(s):  
Wei Hanbing ◽  
Wu Yanhong ◽  
Chen Xing ◽  
Xu Jin ◽  
Rahul Sharma
Keyword(s):  
Driving Simulator ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Least Square ◽  
Shared Control ◽  
Driving Safety ◽  
Dynamic State ◽  
Commercial Application ◽  
Steering Torque

Over a long period of time, the fully autonomous vehicle is far from commercial application. The concept of ‘human-vehicle shared control (HVSC)’ provides a promising solution to enhance autonomous driving safety. In order to characterize the evolution of the driver’s feature in the process of HVSC, a dynamics model of HVSC with the driver’s neuromuscular characteristic is proposed in this paper. It takes into account the driver’s neuromuscular characteristics, such as stretch reflection, feedback stiffness, etc. By designing a model predictive control (MPC) controller, the feedback of the vehicle’s state and steering torque is constructed. For validation of the model, driving simulation has been conducted in our table-based driving simulator. The vehicle state and the surface electromyography of the driver’s arm working muscle group are collected simultaneously. Subsequently, the hierarchical least square (HLS) parameter identification and unscented Kalman filter (UKF) observer is used to identify and estimate the important characteristic parameters respectively based on the experimental results. The comparisons show that the HVSC can characterize the vehicle’s dynamic state and the driver’s personalized characteristic can be identified by HLS. This paper will serve as a theoretical basis of control strategy allocation between the human and vehicle during shared control for L3 class autonomous vehicle.

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 Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2342 ◽  
Author(s):  
Pengwei Wang ◽  
Song Gao ◽  
Liang Li ◽  
Binbin Sun ◽  
Shuo Cheng
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Autonomous Driving ◽  
Field Method ◽  
Driving Safety ◽  
Artificial Potential Field ◽  
Field Range ◽  
Human Driver ◽  
Planning Approach

Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are also verified.

scholarly journals Intersection Vehicle Turning Control for Fully Autonomous Driving Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3995
Author(s):  
Zhizhong Ding ◽  
Chao Sun ◽  
Momiao Zhou ◽  
Zhengqiong Liu ◽  
Congzhong Wu
Keyword(s):  
Video Processing ◽  
Ad Hoc ◽  
High Reliability ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Driver Assistance Systems ◽  
Positioning Systems ◽  
Strategy Model ◽  
Road Intersections ◽  
Turning Control

Currently the research and development of autonomous driving vehicles (ADVs) mainly consider the situation whereby manual driving vehicles and ADVs run simultaneously on lanes. In order to acquire the information of the vehicle itself and the environment necessary for decision-making and controlling, the ADVs that are under development now are normally equipped with a lot of sensing units, for example, high precision global positioning systems, various types of radar, and video processing systems. Obviously, the current advanced driver assistance systems (ADAS) or ADVs still have some problems concerning high reliability of driving safety, as well as the vehicle’s cost and price. It is certain, however, that in the future there will be some roads, areas or cities where all the vehicles are ADVs, i.e., without any human driving vehicles in traffic. For such scenarios, the methods of environment sensing, traffic instruction indicating, and vehicle controlling should be different from that of the situation mentioned above if the reliability of driving safety and the production cost expectation is to be improved significantly. With the anticipation that a more sophisticated vehicle ad hoc network (VANET) should be an essential transportation infrastructure for future ADV scenarios, the problem of vehicle turning control based on vehicle to everything (V2X) communication at road intersections is studied. The turning control at intersections mainly deals with three basic issues, i.e., target lane selection, trajectory planning and calculation, and vehicle controlling and tracking. In this paper, control strategy, model and algorithms are proposed for the three basic problems. A model predictive control (MPC) paradigm is used as the vehicle upper layer controller. Simulation is conducted on the CarSim-Simulink platform with typical intersection scenes.

scholarly journals AmbientBreath

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
Jinmo Lee ◽  
Neska Elhaouij ◽  
Rosalind Picard
Keyword(s):  
Background Noise ◽  
Sensory Feedback ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
User Acceptance ◽  
Driving Safety ◽  
Just In Time ◽  
Breathing Rate ◽  
Safety Measures ◽  
Driving Task

To promote calm breathing inside a car, we designed a just-in-time breathing intervention stimulated by multi-sensory feedback and evaluated its efficacy in a driving simulator. Efficacy was measured via reduction in breathing rate as well as by user acceptance and driving safety measures. Drivers were first exposed to demonstrations of three kinds of ambient feedback designed to stimulate a goal breathing rate: (1) auditory (rhythmic background noise), (2) synchronized modulation of wind (dashboard fans modulating air pointed toward the driver) together with auditory, or (3) synchronized visual (ambient lights) together with auditory. After choosing one preference from these three, each driver engaged in a challenging driving task in a car simulator, where the ambient stimulation was triggered when their breathing exceeded a goal rate adapted to their personal baseline. Two user studies were conducted in a car simulator involving respectively 23 and 31 participants. The studies include both manual and autonomous driving scenarios to evaluate drivers' engagement in the intervention under different cognitive loads. The most frequently selected stimulation was the combined auditory and wind modalities. Measures of changes in breathing rate show that the participants were able to successfully engage in the breathing intervention; however, several factors from the driving context appear to have an impact on when the intervention is or is not effective.

scholarly journals Effective Techniques for Pedestrian Detection in Smart Autonomous Vehicles

Webology ◽  
2021 ◽  
Vol 18 (05) ◽  
pp. 1176-1183
Author(s):  
Thylashri S ◽  
Manikandaprabu N ◽  
Jayakumar T ◽  
Vijayachitra S ◽  
Kiruthiga G
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Pedestrian Detection ◽  
Autonomous Driving ◽  
The Elderly ◽  
Driving Safety ◽  
Research Trend ◽  
Automatic Identification ◽  
Smart Vehicles ◽  
Slowing Down

Pedestrians are essential objects in computer vision. Pedestrian detection in images or videos plays an important role in many applications such as real-time monitoring, counting pedestrians at various events, detecting falls of the elderly, etc. It is formulated as a problem of the automatic identification and location of pedestrians in pictures or videos. In real images, the art of pedestrian detection is an important task for major applications such as video surveillance, autonomous driving systems, etc. Pedestrian detection is also an important feature of the autonomous vehicle driving system because it identifies pedestrians and minimizes accidents between vehicles and pedestrians. The research trend in the field of vehicle electronics and driving safety, vision-based pedestrian recognition technologies for smart vehicles have established themselves loudly or slowing down the vehicle. In general, the visual pedestrian detection progression capable of be busted down into three consecutive steps: pedestrian detection, pedestrian recognition, and pedestrian tracking. There is also visual pedestrian recognition in the vehicle. Finally, we study the challenges and evolution of research in the future.

scholarly journals A Novel Approach to Global Positioning System Accuracy Assessment, Verified on LiDAR Alignment of One Million Kilometers at a Continent Scale, as a Foundation for Autonomous Driving Safety Analysis

2021 ◽  
Author(s):  
Janusz Bedkowski ◽  
Hubert Nowak ◽  
Blazej Kubiak ◽  
Witold Studzinski ◽  
Maciej Janeczek ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Accuracy Assessment ◽  
Autonomous Driving ◽  
Safety Analysis ◽  
Driving Safety ◽  
Positioning System ◽  
Mobile Mapping ◽  
Data Alignment ◽  
Novel Approach ◽  
Global Positioning

This paper concerns a new methodology for accuracy assessment of global positioning system verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of GPS (Global Positioning System) positioning of an autonomous driving vehicle within a lane on the road is one of the key safety considerations. Safety is addressed as a geometry of the problem, where the aim is to maintain knowledge that the vehicle (its bounding box) is within its lane. Accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. For this reason, a new methodology is proposed. Methodology is composed of six elements: 1) Mobile mapping system minimal setup, 2) Global positioning data processing, 3) LiDAR data processing, 4) Alignment algorithm, 5) Accuracy assessment confirmation and 6) Autonomous driving safety analysis. The research challenge is to assess positioning accuracy of moving cars taking into account the constraints of the coverage of limited access highways in the United States of America. The available coverage limits the possibility of repeatable measurements and introduces an important challenge being the lack the ground truth data. State-of-the-art methods are not applicable for this particular application, therefore a novel approach is proposed. The method is to align all the available LiDAR car trajectories to confirm the GNSS+INS (Global Navigation Satellite System + Inertial Navigation System) accuracy. For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. SLAM implementation used state-of-the-art observation equations and the Weighted Non-Linear Least Square optimization technique that enables integration of the required constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS+INS, LiDAR Velodyne HDL32) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32785 trajectories with total length of 1,159,956.9~km and of total $186.4*10^{9}$~optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016--2019 period. It is demonstrated that the alignment improves the trajectories, thus final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99\% of the assessed data satisfies the safety requirements (driving within lanes of 3.6~m) for Mid-Size (width 1.85~m, length 4.87~m) vehicle and 95\% for 6-Wheel Pickup (width 2.03--2.43~m, length 5.32--6.76~m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS+INS accuracy confirmation. Further research is needed to solve the identified challenges.

Geräusch- und Vibrationsbekämpfung in modernen Fahrzeugen/Noise and vibration control in modern vehicles and new challenges due to the rise of e-mobility

2020 ◽  
Vol 15 (06) ◽  
pp. 194-198
Author(s):  
E. Kruse
Keyword(s):  
Vibration Control ◽  
Fuel Consumption ◽  
Automotive Industry ◽  
Well Being ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Control Technology ◽  
Additional Costs ◽  
New Challenges ◽  
Conflicting Goals

Zusammenfassung Weniger Kraftstoffverbrauch bei besseren Fahrleistungen, mehr Komfort und Fahrsicherheit ohne Mehrkosten sowie der Wandel zur Elektromobilität und zum autonomen Fahren: All diese Herausforderungen halten die Automobilindustrie konstant in Bewegung. Neben der technischen Umsetzung haben die Veränderungen Einfluss auf erzeugte Geräusche und Vibrationen und so auf den Komfort und das Wohlbefinden aller Fahrzeuginsassen. Automobilhersteller stehen daher gleich vor mehreren Zielkonflikten. Hier sind neue Lösungsansätze der automobilen Schwingungstechnik gefragt, um auch in der elektrifizierten und autonomen Zukunft komfortabel, entspannt und sicher anzukommen. SUMMARY Lower fuel consumption with better driving performance, more comfort and driving safety without additional costs as well as the change to electromobility and autonomous driving: All these challenges keep the automotive industry constantly on the move. In addition to the technical implementation, the changes have an impact on generated noise and vibrations and thus on the comfort and well-being of all vehicle occupants. Vehicle manufacturers are therefore faced with several conflicting goals. This calls for new approaches in automotive vibration control technology in order to arrive comfortably, relaxed and safely in the electrified and autonomous future.

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