The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Wang, Jianqiang; Wu, Jian; Li, Yang

doi:10.1109/tits.2015.2401837

Cited by 224 publications

(94 citation statements)

References 29 publications

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“…To make the model more intuitive and reasonable, the traffic risk is described by assigning an equivalent force vector to each point of a map by using this method. The norm of equivalent force vector represents the state of the risk at that point, which is similar to our previous research (Wang et al, 2014(Wang et al, , 2015(Wang et al, , 2016. Compared to our presented study, the principle of modeling has changed.…”

Section: Discussionsupporting

confidence: 76%

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A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

Zheng

Huang

et al. 2018

JICV

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Purpose-The purpose of this paper is to accurately capture the risks which are caused by each road user in time. Design/methodology/approach-The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle-vehicle and vehicle-road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area. Findings-The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance. Originality/value-This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle-vehicle and vehicle-road were presented and theoretically calculated.

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Section: Discussionsupporting

confidence: 76%

“…Our previous research (Wang et al, 2014(Wang et al, , 2015(Wang et al, , 2016) presented a driving safety field theory based on the artificial potential field concept. This method is able to represent risks of driving caused by drivers, vehicles, roads and other traffic factors.…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

Zheng

Huang

et al. 2018

JICV

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“…Some complex mathematic models were proposed considering dynamic features, such as the safety field model [19]. These models firstly defined the relationship between features and SA models and then calibrated the parameters of the function using realistic driving data [20]. The driving safety model was used and proved in the pre-collision warning system.…”

Section: Related Workmentioning

confidence: 99%

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

et al. 2017

Self Cite

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Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics-and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs' plan motion trajectories and make high-level decisions in uncertain environments.

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“…It plans the path by moving in the descent direction of the field. Then, a path tracking module calculates the vehicle inputs required to track the path [5,6]. The main advantage of this method over the other path planning methods is its low calculation cost even with complex PFs for obstacles and road structures.…”

Section: Introductionmentioning

confidence: 99%

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Rasekhipour

Khajepour

Chen

et al. 2017

IEEE Trans. Intell. Transport. Syst.

483

194

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Abstract-Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures, and plans the path based on these potential functions. It does not however, include the vehicle dynamics in the path planning process. On the other hand, an optimal path planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints, and not with any arbitrary function. A model predictive path planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path planning system plans paths corresponding to their importance and priorities.Index Terms-Path Planning, Autonomous Vehicles, Road Vehicles, Model Predictive Control, Artificial Potential Field, Vehicle Dynamics and Control.

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The Driving Safety Field Based on Driver–Vehicle–Road Interactions

Cited by 224 publications

References 29 publications

A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

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