The Software Stack That Won the Formula Student Driverless Competition

TL;DR

This paper presents a comprehensive software stack for an autonomous race car, integrating perception, mapping, planning, and control to achieve competitive performance in the Formula Student Driverless competition.

Contribution

It introduces a novel integrated software architecture utilizing LiDAR, cameras, GraphSLAM, and advanced control methods for high-speed autonomous racing.

Findings

Reliable cone detection at 35 m distance

High-precision mapping with <15 cm error at 70 kph

Successful real-world testing on race track

Abstract

This report describes our approach to design and evaluate a software stack for a race car capable of achieving competitive driving performance in the different disciplines of the Formula Student Driverless. By using a 360{\deg} LiDAR and optionally three cameras, we reliably recognize the plastic cones that mark the track boundaries at distances of around 35 m, enabling us to drive at the physical limits of the car. Using a GraphSLAM algorithm, we are able to map these cones with a root-mean-square error of less than 15 cm while driving at speeds of over 70 kph on a narrow track. The high-precision map is used in the trajectory planning to detect the lane boundaries using Delaunay triangulation and a parametric cubic spline. We calculate an optimized trajectory using a minimum curvature approach together with a GGS-diagram that takes the aerodynamics at different velocities into account. To track the target path with accelerations of up to 1.6 g, the control system is split into a PI controller for longitudinal control and model predictive controller for lateral control. Additionally, a low-level optimal control allocation is used. The software is realized in ROS C++ and tested in a custom simulation, as well as on the actual race track.