GAMEOPT: Optimal Real-time Multi-Agent Planning and Control for Dynamic Intersections
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Abstract (arXiv)
We propose GameOpt: a novel hybrid approach to cooperative intersection control for dynamic, multi-lane, unsignalized intersections. Safely navigating these complex and accident prone intersections requires simultaneous trajectory planning and negotiation among drivers. GameOpt is a hybrid formulation that first uses an auction mechanism to generate a priority entrance sequence for every agent, followed by an optimization-based trajectory planner that computes velocity controls that satisfy the priority sequence. This coupling operates at real-time speeds of less than 10 milliseconds in high density traffic of more than 10,000 vehicles/hr, 100 times faster than other fully optimization-based methods, while providing guarantees in terms of fairness, safety, and efficiency. Tested on the SUMO simulator, our algorithm improves throughput by at least 25%, time taken to reach the goal by 75%, and fuel consumption by 33% compared to auction-based approaches and signaled approaches using traffic-lights and stop signs.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2022.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 2.
8 papers.
- 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 56
- 2022Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Driverscited by 7
- 2022SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigationcited by 6
- 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowdscited by 6
- 2023SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spacescited by 5
- 2023Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimizationcited by 2
- 2025Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RLcited by 1
- 2025Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Drivingcited by 0
- 2025Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL16 Nov 2025
- 2025Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Driving16 Nov 2025
- 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds26 May 2024
- 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithms29 Jun 2023
- 2023Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization15 Jun 2023
- 2023SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces9 Mar 2023
- 2022SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation15 Oct 2022
- 2022Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers1 May 2022