Longitudinal Control Algorithm for Cooperative Autonomous Vehicles to Avoid Accident with Vulnerable Road Users

Abstract

The cooperative perception among connected autonomous vehicles extends the field-of-view of the individual cars and adds significantly to their sensing and collision avoidance capabilities. This feature is particularly useful and essential in avoiding collisions with pedestrians, vulnerable road users, and other objects or cars which are obscured in the typical field-of-view of an ego vehicle. This paper proposes a simple to implement but effective longitudinal control algorithm to avoid collisions in a dynamic environment for cooperative autonomous vehicles. The algorithm is applied to ego and lead vehicles to control longitudinal dynamics with appropriate braking based on safety distance modeling. Simulations using dynamic models for both vehicles and pedestrians on a hazardous traffic scenario are presented to illustrate the effectiveness of the proposed control algorithm. The proposed method is also capable of warning and avoiding collisions for several other critical situations that may appear in autonomous driving. The results demonstrate a promising solution for cooperative collision avoidance, which can be further expanded to more complex scenarios.