Risk-Aversive Behavior Planning under Multiple Situations with Uncertainty

Damerow, Florian; Eggert, Julian

doi:10.1109/itsc.2015.113

Cited by 32 publications

(20 citation statements)

References 10 publications

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“…Meanwhile, the ACV should stay within the drivable area, as shown in Eq. (26). ACV should keep a safe distance with SVs.…”

Section: ) Motion Prediction Of Acvmentioning

confidence: 99%

“…Hruschka et al used maneuver-based motion models to predict the motion of SVs, and evaluated the collision risk with a combination of collision probability and collision severity [25]. In literature [26], the risk is defined as the expectation value of the cost related to a future critical event. Future event probability and damage probability are used to determine risk indicator for motion planning of ego vehicle.…”

Section: Introductionmentioning

confidence: 99%

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Surrounding Vehicles Motion Prediction for Risk Assessment and Motion Planning of Autonomous Vehicle in Highway Scenarios

et al. 2020

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Safety is the cornerstone of autonomous driving vehicles. For autonomously controlled vehicles driving safely in complex and dynamic traffic scenarios, it is essential to predict the evolution of the current traffic situation in the near future and make an accurate situation risk assessment. The precise motion prediction of surrounding vehicles is an essential prerequisite for risk assessment and motion planning of autonomous vehicles. In this paper, we propose a risk assessment and motion planning method for autonomously controlled vehicles based on motion prediction of surrounding vehicles. Firstly, surrounding vehicles' trajectories are predicted based on fusing constant turn rate and acceleration-based motion prediction model and maneuver-based motion prediction model with interactive multiple models. Then, the collision risk assessment between autonomously controlled vehicle and surrounding vehicles is conducted with a collision risk index considering both the probability of collision event and collision severity. After that, the motion planning of autonomously controlled vehicle is formulated as a multiobjectives and multi-constraints optimization problem with model predictive control. Finally, the proposed method is applied to several traffic scenarios to validate its feasibility and effectiveness. INDEX TERMS Autonomous vehicles, motion prediction, risk assessment, motion planning, model predictive control.

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“…Meanwhile, the ACV should stay within the drivable area, as shown in Eq. (26). ACV should keep a safe distance with SVs.…”

Section: ) Motion Prediction Of Acvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surrounding Vehicles Motion Prediction for Risk Assessment and Motion Planning of Autonomous Vehicle in Highway Scenarios

et al. 2020

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“…However, there seems to be little research on interaction-capable motion planning except for the following. Damerow and Eggert [56] present a so-called predictive risk map, which measures the risks of different manoeuvres. Then, the RRT* algorithm is used to find an optimal trajectory with the lowest risk.…”

Section: Predictive Motion Planningmentioning

confidence: 99%

An Interaction-Aware Predictive Motion Planner for Unmanned Ground Vehicles in Dynamic Street Scenarios

Li¹,

Dai²,

Li³

et al. 2019

International Journal of Robotics and Automation

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An interaction-aware predictive motion planning method for unmanned ground vehicles is presented in dynamic street scenarios. Although trajectory prediction in motion planners is widely covered in the past few years, most of them only consider the physical model of the vehicles and ignore the interaction among vehicles. Our motion planner predicts the future trajectories of surrounding participant vehicles taking the traffic interaction and manoeuvres into consideration. Furthermore, the motion planner exploits an improved trajectory generation method. The kinematically feasible trajectories are generated, which prevents a long-term collision using the predicted results in a probabilistic manner. The results show that our motion planner improves the safety and smoothness of driving trajectories in interactive scenarios.

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“…There are many works in the autonomous driving literature that look at risk-averse driving [17] and risk assessment [18], [17], [19]. Prediction is often used for safety in autonomous driving and accurate prediction models are a current topic of research in the autonomous driving community [16].…”

Section: Application To Autonomous Drivingmentioning

confidence: 99%

Safe Reinforcement Learning on Autonomous Vehicles

Isele

Nakhaei

Fujimura

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications. Recent work in safe reinforcement learning uses idealized models to achieve their guarantees, but these models do not easily accommodate the stochasticity or high-dimensionality of real world systems. We investigate how prediction provides a general and intuitive framework to constraint exploration, and show how it can be used to safely learn intersection handling behaviors on an autonomous vehicle.

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Risk-Aversive Behavior Planning under Multiple Situations with Uncertainty

Cited by 32 publications

References 10 publications

Surrounding Vehicles Motion Prediction for Risk Assessment and Motion Planning of Autonomous Vehicle in Highway Scenarios

Surrounding Vehicles Motion Prediction for Risk Assessment and Motion Planning of Autonomous Vehicle in Highway Scenarios

An Interaction-Aware Predictive Motion Planner for Unmanned Ground Vehicles in Dynamic Street Scenarios

Safe Reinforcement Learning on Autonomous Vehicles

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