Competitive Driving of Autonomous Vehicles

TL;DR

This paper presents a simple yet effective autonomous racing controller for high-speed multi-vehicle competitions, successfully enabling collision-free, competitive driving in simulated Indy Autonomous Challenge races.

Contribution

It introduces a novel online maneuver selection method that balances progress maximization with collision avoidance using a point mass model.

Findings

Successfully competed in IAC simulation race without collisions

Achieved competitive driving performance at 300 km/h

Demonstrated effectiveness of simple control approach

Abstract

This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, each tested for collision with the opponent vehicles. Finally, it selects the maneuver that maximizes progress along the track, taking into account the race line. The maneuver candidates, as well as the predicted trajectories of the opponent vehicles, are approximated using a point mass model. Despite the simplicity of this racing controller, it managed to drive competitively and with no collision with any of the opponent vehicles in the IAC final simulation race.