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Hierarchical Control for Head-to-Head Autonomous Racing by Thakkar, Rishabh Saumil; Samyal, Aryaman Singh; Fridovich-Keil, David; Xu, Zhe; Topcu, Ufuk is a scholarly article available to read on EtoBox.

What is Hierarchical Control for Head-to-Head Autonomous Racing about?

We develop a hierarchical controller for head-to-head autonomous racing. We first introduce a formulation of a racing game with realistic safety and fairness rules. A high-level planner approximates the original formulation as a discrete game with simplified state, control, and dynamics to easily encode the complex safety and fairness rules and calculates a series of target waypoints. The low-level controller takes the resulting waypoints as a reference trajectory and computes high-resolution control inputs by solving an alternative formulation approximation with simplified objectives and constraints. We consider two approaches for the low-level planner, constructing two hierarchical controllers. One approach uses multi-agent reinforcement learning (MARL), and the other solves a linear-quadratic Nash game (LQNG) to produce control inputs. The controllers are compared against three baselines: an end-to-end MARL controller, a MARL controller tracking a fixed racing line, and an LQNG controller tracking a fixed racing line. Quantitative results show that the proposed hierarchical methods outperform their respective baseline methods in terms of head-to-head race wins and abiding by the

Author

Thakkar, Rishabh Saumil; Samyal, Aryaman Singh; Fridovich-Keil, David; Xu, Zhe; Topcu, Ufuk

Published

2022

Language

EN