Lipschitz Safe Bayesian Optimization for Automotive Control

Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes, pp. 3782–3788

Abstract

Controller tuning is a labor-intensive process that requires human intervention and expert knowledge. Bayesian optimization has been applied successfully in different fields to automate this process. However, when tuning on hardware, such as in automotive applications, strict safety requirements often arise. To obtain safety guarantees, many existing safe Bayesian optimization methods rely on assumptions that are hard to verify in practice. This leads to the use of unjustified heuristics in many applications, which invalidates the theoretical safety guarantees. Furthermore, applications often require multiple safety constraints to be satisfied simultaneously. Building on recently proposed Lipschitz-only safe Bayesian optimization, we develop an algorithm that relies on readily interpretable assumptions and satisfies multiple safety constraints at the same time. We apply this algorithm to the problem of automatically tuning a trajectory-tracking controller of a self-driving car. Results both from simulations and an actual test vehicle underline the algorithm’s ability to learn tracking controllers without leaving the track or violating any other safety constraints.

Authors 4

1. Johanna Menn Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering (DSME) RWTH Aachen University,Aachen,Germany,52068

2. Pietro Pelizzari

   ZF Friedrichshafen (Germany)

   Affiliation as printed

   Engineering Solutions ZF Friedrichshafen AGins,Friedrichshafen,Germany,88045

3. Michael Fleps-Dezasse

   ZF Friedrichshafen (Germany)

   Affiliation as printed

   Engineering Solutions ZF Friedrichshafen AGins,Friedrichshafen,Germany,88045

4. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering (DSME) RWTH Aachen University,Aachen,Germany,52068