Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
This paper presents a new system for helping self-driving cars coordinate at intersections without traffic signals. The system uses a central agent to create a plan that guides individual cars on how to move safely and efficiently.
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- 1 The proposed system, MAPS, helps autonomous vehicles navigate intersections without collisions.
- 2 It uses a central plan that simplifies communication between vehicles.
- 3 The system has been tested in various scenarios and shows improved travel times compared to existing methods.
Introduction
The introduction discusses the challenges of coordinating multiple autonomous vehicles at unsignalized intersections, emphasizing the need for collision-free navigation and throughput. It critiques existing multi-agent deep reinforcement learning (MADRL) methods for their limited generalization, reliance on privileged information, and fixed action spaces. The proposed MAPS architecture aims to address these issues by using proto-plans for high-level coordination.
MARL for Autonomous Driving
The section focuses on the application of MARL in autonomous driving, detailing various algorithms and frameworks that enhance safety, efficiency, and responsiveness. It highlights the importance of hierarchical architectures and critiques existing methods for their combinatorial action spaces.
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Overview
An overview of the MAPS architecture is provided, describing its hierarchical Markov decision process structure. The centralized Master agent generates a continuous proto-plan embedding, which is used by decentralized Worker agents to select vehicle-specific actions, thus avoiding combinatorial scaling of action spaces.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1 :: Figure 1: Hierarchical MARL framework. The Master agent observes global state s Mt and generates a proto-plan embedding z t . Each Worker i receives z t along with its local observation to produce vehiclespecific actions a i t .
- Figure 2: (a) Initial configuration: five vehicles approach from all directions with varying distances to the intersection.(b) Conflict zone: vehicles converge at the intersection center, requiring coordinated timing to avoid collisions.
- Figure 2 :: Figure 2: Example scenarios from the HighwayEnv environment, illustrating initial vehicle placement and the resulting high-density conflict zone.
- Figure 4: Training Collision Count (CC): The total number of collision events across all training episodes, CC = K k=1 c k where c k \u2208 {0, 1}. This tracks learning safety, where lower values indicate fewer dangerous experiences during training. \u2022 Cumulative Episode Reward (R): The total reward accumulated over an episode, R = T t=1 r t . Higher values indicate better combined safety and efficiency. \u2022 Average Travel Time (AT T ): The mean number of simulation steps for active learning agents to traverse the intersection: AT T = 1 |Wactive| |Wactive| i=1.
- Figure 3 :: Figure 3: Training dynamics comparing MAPS and baselines: (a) success rate, (b) collision rate, (c) cumulative episode reward, and (d) average travel time. Moving average window size: 20.
Frequently Asked Questions
This paper presents a new system for helping self-driving cars coordinate at intersections without traffic signals. The system uses a central agent to create a plan that guides individual cars on how to move safely and efficiently.
The introduction discusses the challenges of coordinating multiple autonomous vehicles at unsignalized intersections, emphasizing the need for collision-free navigation and throughput. It critiques existing multi-agent deep reinforcement learning (MADRL) methods for their.
The proposed system, MAPS, helps autonomous vehicles navigate intersections without collisions. It uses a central plan that simplifies communication between vehicles. The system has been tested in various scenarios and shows improved travel times compared to existing methods.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.