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Courteous MPC for Autonomous Driving with CBF-inspired Risk Assessment

Yanze Zhang,Yiwei Lyu, Sude E. Demir,Xingyu Zhou, Yupeng Yang,Junmin WangTop Scholar,Wenhao Luo

CoRR(2024)

Cited 0|Views6
Abstract
With more autonomous vehicles (AVs) sharing roadways with human-driven vehicles (HVs), ensuring safe and courteous maneuvers that respect HVs' behavior becomes increasingly important. To promote both safety and courtesy in AV's behavior, an extension of Control Barrier Functions (CBFs)-inspired risk evaluation framework is proposed in this paper by considering both noisy observed positions and velocities of surrounding vehicles. The perceived risk by the ego vehicle can be visualized as a risk map that reflects the understanding of the surrounding environment and thus shows the potential for facilitating safe and courteous driving. By incorporating the risk evaluation framework into the Model Predictive Control (MPC) scheme, we propose a Courteous MPC for ego AV to generate courteous behaviors that 1) reduce the overall risk imposed on other vehicles and 2) respect the hard safety constraints and the original objective for efficiency. We demonstrate the performance of the proposed Courteous MPC via theoretical analysis and simulation experiments.
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Key words
Autonomous Vehicles,Model Predictive Control,Risk Perception,Simulation Experiments,Risk Evaluation,Risk Map,Control Barrier,Time Step,Scaling Factor,Highway,Cost Function,Control Input,Hidden Markov Model,Changes In Velocity,Social Orientation,Vehicle Position,System Of Eqs,Safety Guarantees,Inverse Reinforcement Learning,Model Predictive Control Framework
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