Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning

The recent development of deep learning has led to dramatic progress in multiple research fields, and this technique has naturally found applications in autonomous vehicles. The use of deep learning to perform perceptive tasks such as image segmentation has been widely researched in the last few years, and highly efficient neural network architectures are now available for such tasks. More recently, several teams have proposed taking deep learning a step further, by training so-called "end-to-end" algorithms to directly output vehicle controls from raw sensor data (see, in particular, the seminal work in [1]). Although end-to-end driving is highly appealing, as it removes the need to design motion planning and control algorithms by hand, handing the safety of the car occupants to a software operating as a black box seems problematic. A possible workaround to this downside is to use "forensics" techniques that can, to a certain extent, help understand the behavior of deep neural networks [2]. We choose a different approach consisting in breaking down complexity by training simpler, mono-task neural networks to solve specific problems arising in autonomous driving; we argue that the reduced complexity of individual tasks allows much easier testing and validation. In this article, we focus on the problem of controlling a car-like vehicle in highly dynamic situations, for instance to perform evasive maneuvers in face of an obstacle. A particular challenge in such scenarios is the important coupling between longitudinal and lateral dynamics when nearing the vehicle's handling limits, which requires highly detailed Published in the IEEE 2018 International Conference on Intelligent Transportation Systems (ITSC 2018). This work was supported by the international Chair MINES ParisTech - Peugeot-Citro en - Safran - V aleo on ground vehicle automation.