Compositionally Safe Construction of Autonomous Driving Systems

Abstract

Developing safe autonomous driving systems is a major scientific and technical challenge. Existing AI-based end-to-end solutions do not offer the necessary safety guarantees, while traditional systems engineering approaches are defeated by the complexity of the problem. We study a method for building compositionally safe autonomous driving systems, based on the assumption that the capability to drive boils down to the coordinated execution of a given set of driving operations. The assumption is substantiated by a compositionality result considering that autopilots are dynamic systems receiving a small number of types of driving configurations as input, each configuration defining a free space in its neighborhood. It is shown that safe driving for each type of configuration in the corresponding free space, implies safe driving for any possible scenario under some easy-to-check conditions concerning the transition between configurations. The designed autopilot comprises distinct control policies one per type of driving configurations, articulated in two consecutive phases. The first phase consists of carefully managing a potentially risky situation by virtually reducing speed, while the second phase consists of exiting the situation by accelerating. The autopilots designed use for their predictions simple functions characterizing the acceleration and deceleration capabilities of the vehicles. They cover the main driving operations, including entering a main road, overtaking, crossing intersections protected by traffic lights or signals, and driving on freeways. The results presented reinforce the case for solutions that incorporate mathematically elegant and robust decision methods that are safe by construction.