scholarly journals Assessing and Explaining Collision Risk in Dynamic Environments for Autonomous Driving Safety

2021 ◽  
Author(s):  
Richa Nahata ◽  
Daniel Omeiza ◽  
Rhys Howard ◽  
Lars Kunze
Keyword(s):  
Autonomous Driving ◽  
Dynamic Environments ◽  
Driving Safety ◽  
Collision Risk

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.

scholarly journals Advanced Adaptive Cruise Control Based on Operation Characteristic Estimation and Trajectory Prediction

2019 ◽  
Vol 9 (22) ◽  
pp. 4875 ◽  
Author(s):  
Hanwool Woo ◽  
Hirokazu Madokoro ◽  
Kazuhito Sato ◽  
Yusuke Tamura ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Adaptive Cruise Control ◽  
Individual Characteristics ◽  
Driving Safety ◽  
Cruise Control ◽  
Collision Risk ◽  
Trajectory Prediction ◽  
Lane Changes ◽  
The Individual ◽  
Near Future ◽  
Real Traffic

In this paper, we propose an advanced adaptive cruise control to evaluate the collision risk between adjacent vehicles and adjust the distance between them seeking to improve driving safety. As a solution for preventing crashes, an autopilot vehicle has been considered. In the near future, the technique to forecast dangerous situations and automatically adjust the speed to prevent a collision can be implemented to a real vehicle. We have attempted to realize the technique to predict the future positions of adjacent vehicles. Several previous studies have investigated similar approaches; however, these studies ignored the individual characteristics of drivers and changes in driving conditions, even though the prediction performance largely depends on these characteristics. The proposed method allows estimating the operation characteristics of each driver and applying the estimated results to obtain the trajectory prediction. Then, the collision risk is evaluated based on such prediction. A novel advanced adaptive cruise control, proposed in this paper, adjusts its speed and distance from adjacent vehicles accordingly to minimize the collision risk in advance. In evaluation using real traffic data, the proposed method detected lane changes with 99.2% and achieved trajectory prediction error of 0.065 m, on average. In addition, it was demonstrated that almost 35% of the collision risk can be decreased by applying the proposed method compared to that of human drivers.

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.

The Prediction of Vehicle Collision Risk in Traffic Conflict Zone Based on Bayesian Network

2015 ◽  
Vol 744-746 ◽  
pp. 1953-1959 ◽  
Author(s):  
Li Xin Yan ◽  
Song Gao ◽  
Hao Cai ◽  
Hui Wan
Keyword(s):  
Bayesian Network ◽  
Traffic Safety ◽  
Driving Safety ◽  
Posteriori Probability ◽  
Vehicle Speed ◽  
Collision Risk ◽  
Traffic Environment ◽  
Traffic Conflict ◽  
Probability Table ◽  
Probability Of Collision

The external traffic environment has a big influence to the traffic safety during the area of traffic conflict place，and to analysis the relationship between the external traffic environment factors and driving safety is helpful to improve the traffic safety. The method of comprehensive analysis the historical data and expert survey data is used to explore this question. And at the same time, the collision risk prediction model during the traffic conflict place is built by the Bayesian network. According to the data analyzing, the node variable, the state of variable and the conditional probability table of this model is also built. Finally, the software of Hugin is used to deal with the posteriori probability of collision risk, and the result proved that this model can predict the collision risk accurately during the traffic conflict area, and the data analyzing showed that the factor of the driver's intention, the vehicle speed and the headway have a significance influence to the traffic safety.

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.

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