Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competition
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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.
Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Apr 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 2; 2025: 1; 2026: 1.
8 papers.
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 338
- 2024Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agentscited by 11
- 2024LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trendscited by 10
- 2023Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizoncited by 4
- 2023aUToLights: A Robust Multi-Camera Traffic Light Detection and Tracking Systemcited by 1
- 2025aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Searchcited by 1
- 2026BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognitioncited by 1
- 2023Does Unpredictability Influence Driving Behavior?cited by 0
- 2026BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition15 Jan 2026
- 2025aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environments Using Hybrid Lattice and Free-Space Search14 May 2025
- 2024LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends20 Oct 2024
- 2024Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents15 Oct 2024
- 2023Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon20 Sep 2023
- 2023Does Unpredictability Influence Driving Behavior?28 Jul 2023
- 2023aUToLights: A Robust Multi-Camera Traffic Light Detection and Tracking System15 May 2023
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning13 Aug 2021