NEURAL-BASED ALGORITHM FOR AUTONOMOUS VEHICLE ROUTE GUIDANCE SYSTEMS

This paper presents a new algorithm for the autonomous vehicles'shortest path computing based on the Hopfield neural networks. Besides the higher computation speed, the algorithm can adapt in real time to changes in network topology and link costs without modifying the internal parameters of the neural net architecture. It can be applied to autonomous vehicles for urban route guidance systems.