We propose a risk-aware crash mitigation system (RCMS), to augment any existing motion planner (MP), that enables an autonomous vehicle to perform evasive maneuvers in high-risk situations and minimize the severity of collision if a crash is inevitable. In order to facilitate a smooth transition between RCMS and MP, we develop a novel activation mechanism that combines instantaneous as well as predictive collision risk evaluation strategies in a unified hysteresis-band approach. For trajectory planning, we deploy a modular receding horizon optimization-based approach that minimizes a smooth situational risk profile, while adhering to the physical road limits as well as vehicular actuator limits. We demonstrate the performance of our approach in a simulation environment.


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    Title :

    RCMS: Risk-Aware Crash Mitigation System for Autonomous Vehicles


    Contributors:


    Publication date :

    2023-09-24


    Size :

    1961469 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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