Integrated Risk Grid Map for Collision Avoidance and Mitigation Maneuvers of Autonomous Vehicle

Integrated Risk Grid Map for Collision Avoidance and Mitigation Maneuvers of Autonomous Vehicle

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As autonomous driving algorithms evolve, algorithms for collision avoidance are being developed for situations that primary algorithms cannot resolve.Generally, the positions of all surrounding vehicles are predicted, and the risk is assessed to create a risk map, which tourettebrewing.com is then used to generate a safe path.Most algorithms focus on collision avoidance without considering scenarios in which avoidance fails and a collision occurs.However, collision avoidance algorithms need to consider imminent collision scenarios and provide information about potential injuries or damage.Therefore, this paper proposes an integrated risk map capable of identifying the safest space, utilizing data on the risk prediction of surrounding vehicles, the severity of collisions in autonomous vehicles, and human injury data.

The integrated risk map comprises two layers: a risk prediction grid map based on relative information with surrounding vehicles and a severity grid map simply southern cat shirt based on collision severity and human injury data in collision zones.These two layers are combined to calculate an integrated risk value, enhancing passenger safety by considering potential damage when generating a collision avoidance path.The proposed integrated risk map was evaluated using the same path generation algorithm and scenarios as those used for risk maps based solely on relative information.The evaluation demonstrated that the integrated risk map selects safer avoidance paths by considering the severity of potential injuries, compared to risk maps that only consider relative information.