Abstract
AbstractThis paper deals with uncertain information of obstacles resulting in from practical obstacle-avoiding techniques for autonomous vehicles. In fact, this problem is ignored in many researches by assumptions that measurements are perfect or the vehicle can fully observe the state of obstacles. A probability model is proposed to evaluate the possibility of collision quantitatively based on the current position of the vehicle and the probability distribution of obstacles’ position. This probability model is then applied to design a new repulsive function. Hence, the resulting artificial potential field can avoid uncertain obstacles by maneuvering the vehicle in the direction of decreasing collision risk. Numerical simulations are carried out to verify the proposed collision avoidance model, and the results show that the proposed method can help autonomous vehicles to efficiently pass obstacles safely with uncertain information.KeywordsAutonomous vehicle (AV)Obstacle avoidanceCollision avoidanceUncertain obstacleProbability-based artificial potential field
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