A Computationally Efficient Neural Dynamics Approach to Trajectory Planning of An Intelligent Vehicle

Real-time safety aware navigation of an intelligent vehicle is one of the major challenges in intelligent vehicle systems. Many studies have been focused on the obstacle avoidance to prevent an intelligent vehicle from approaching obstacles "too close" or "too far", but difficult to obtain an optimal trajectory. In this paper, a novel biologically inspired neural network methodology with safety consideration to real-time collision-free navigation of an intelligent vehicle with safety consideration in a non-stationary environment is proposed. The real-time vehicle trajectory is planned through the varying neural activity landscape, which represents the dynamic environment, in conjunction of a safety aware navigation algorithm. The proposed model for intelligent vehicle trajectory planning with safety consideration is capable of planning a real-time "comfortable" trajectory by overcoming the either "too close" or "too far" shortcoming. Simulation results are presented to demonstrate the effectiveness and efficiency of the proposed methodology that performs safer collision-free navigation of an intelligent vehicle.