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Lateral and Longitudinal Motion Control of Autonomous Vehicles using Deep Learning

The Current trend of the automotive industry combined with active research by the major tech companies has proven that self-driving vehicles are the future. The biggest challenge for self-driving cars is autonomous lateral and longitudinal control. An end-to-end model seems very promising in providing a complete software stack for autonomous driving. The work described in this paper focuses on how a deep learning technique is utilized for implementing both lateral and longitudinal control of vehicles. The open racing car simulator (TORCS) is used for developing and testing the implementation. Two separate neural networks were trained that can predict the vehicle speed and steering based on the road trajectory. Such an approach serves as a foundation towards building a system that utilizes artificial intelligence to analyze the environment and determine what the vehicle speed should be rather than following a set of predetermined rules.