Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

Autonomous driving is one of the most interesting fields of research, with a number of important applications, like agricultural, military and, most significantly, safety. This paper addresses the problem of designing a general purpose path planner and its associated low level control for autonomous vehicles operating in unknown environments. Different kinds of inputs, like the results of obstacle detection, ditch localization, lane detection, and global path planning information are merged together using potential fields to build a representation of the environment in real-time; kinematically feasible trajectories, based on vehicle dynamics, are generated on a cost map. This approach demonstrated both flexibility and reliability for vehicle driving in very different environments, including extreme road conditions. This controller was extensively tested during VIAC, the VisLab Intercontinental Autonomous Challenge, a 13,000 km long test for intelligent vehicle applications. The results, collected during the development stage and the experiment itself, are presented in the final part of this article.