IIT Bombay Racing Driverless: Autonomous Driving Stack for Formula Student AI

TL;DR

This paper details the development of an autonomous racing car for Formula Student AI, utilizing advanced sensors and deep learning algorithms to navigate complex tracks without human input, tested through simulations and small-scale experiments.

Contribution

The paper introduces an integrated autonomous driving stack specifically designed for Formula Student AI racing, combining sensor fusion, deep learning, and control systems.

Findings

Successful perception and localization in complex environments

Effective path planning and control algorithms demonstrated

Validated performance through simulations and small-scale tests

Abstract

This work presents the design and development of IIT Bombay Racing's Formula Student style autonomous racecar algorithm capable of running at the racing events of Formula Student-AI, held in the UK. The car employs a cutting-edge sensor suite of the compute unit NVIDIA Jetson Orin AGX, 2 ZED2i stereo cameras, 1 Velodyne Puck VLP16 LiDAR and SBG Systems Ellipse N GNSS/INS IMU. It features deep learning algorithms and control systems to navigate complex tracks and execute maneuvers without any human intervention. The design process involved extensive simulations and testing to optimize the vehicle's performance and ensure its safety. The algorithms have been tested on a small scale, in-house manufactured 4-wheeled robot and on simulation software. The results obtained for testing various algorithms in perception, simultaneous localization and mapping, path planning and controls have been detailed.