# Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competition

arXiv: https://arxiv.org/abs/2004.08752 · HTML: https://mlsanity.com/paper/2004.08752/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents](https://mlsanity.com/paper/2410.11671/) (2024)
- [LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends](https://mlsanity.com/paper/2410.15281/) (2024)
- [Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon](https://mlsanity.com/paper/2309.11453/) (2023)
- [aUToLights: A Robust Multi-Camera Traffic Light Detection and Tracking System](https://mlsanity.com/paper/2305.08673/) (2023)
- [aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search](https://mlsanity.com/paper/2505.09475/) (2025)
- [BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition](https://mlsanity.com/paper/2601.10521/) (2026)
- [Does Unpredictability Influence Driving Behavior?](https://mlsanity.com/paper/2307.15287/) (2023)

## Abstract (arXiv)

The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock town built for self-driving car testing at the University of Michigan. Teams were required to autonomously navigate a series of intersections while handling pedestrians, traffic lights, and traffic signs. Zeus is aUToronto's winning entry in the AutoDrive Challenge. This article describes the system design and development of Zeus as well as many of the lessons learned along the way. This includes details on the team's organizational structure, sensor suite, software components, and performance at the Year 2 competition. With a team of mostly undergraduates and minimal resources, aUToronto has made progress towards a functioning self-driving vehicle, in just two years. This article may prove valuable to researchers looking to develop their own self-driving platform.