MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation

• URL: http://arxiv.org/abs/2008.07081v2
• Date: Tue, 23 Mar 2021 05:05:22 GMT
• Title: MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation
• Authors: Xiaoyi Chen, Pratik Chaudhari
• Abstract summary: This paper builds a reinforcement learning-based method named MIDAS where an ego-agent learns to affect the control actions of other cars. MIDAS is validated using extensive experiments and we show that it (i) can work across different road geometries, (ii) is robust to changes in the driving policies of external agents, and (iv) is more efficient and safer than existing approaches to interaction-aware decision-making.
• Score: 22.594295184455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous navigation in crowded, complex urban environments requires interacting with other agents on the road. A common solution to this problem is to use a prediction model to guess the likely future actions of other agents. While this is reasonable, it leads to overly conservative plans because it does not explicitly model the mutual influence of the actions of interacting agents. This paper builds a reinforcement learning-based method named MIDAS where an ego-agent learns to affect the control actions of other cars in urban driving scenarios. MIDAS uses an attention-mechanism to handle an arbitrary number of other agents and includes a "driver-type" parameter to learn a single policy that works across different planning objectives. We build a simulation environment that enables diverse interaction experiments with a large number of agents and methods for quantitatively studying the safety, efficiency, and interaction among vehicles. MIDAS is validated using extensive experiments and we show that it (i) can work across different road geometries, (ii) results in an adaptive ego policy that can be tuned easily to satisfy performance criteria such as aggressive or cautious driving, (iii) is robust to changes in the driving policies of external agents, and (iv) is more efficient and safer than existing approaches to interaction-aware decision-making.

Related papers

• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Fact-based Agent modeling for Multi-Agent Reinforcement Learning [6.431977627644292]
  Fact-based Agent modeling (FAM) method is proposed in which fact-based belief inference (FBI) network models other agents in partially observable environment only based on its local information. We evaluate FAM on various Multiagent Particle Environment (MPE) and compare the results with several state-of-the-art MARL algorithms.
  arXiv  Detail & Related papers  (2023-10-18T19:43:38Z)
• Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning [0.0]
  We develop risk shadowing, a situation understanding method that allows us to go beyond single interactions. We show that using risk shadowing as an upstream filter module for a behavior planner allows to plan more decisive and comfortable driving strategies.
  arXiv  Detail & Related papers  (2023-07-20T09:16:01Z)
• Robust Driving Policy Learning with Guided Meta Reinforcement Learning [49.860391298275616]
  We introduce an efficient method to train diverse driving policies for social vehicles as a single meta-policy. By randomizing the interaction-based reward functions of social vehicles, we can generate diverse objectives and efficiently train the meta-policy. We propose a training strategy to enhance the robustness of the ego vehicle's driving policy using the environment where social vehicles are controlled by the learned meta-policy.
  arXiv  Detail & Related papers  (2023-07-19T17:42:36Z)
• Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction [39.48631437946568]
  This paper focuses on the complex task of merging into moving traffic where uncertainty emanates from the behavior of other drivers. We frame the problem as a partially observable Markov decision process (POMDP) and solve it online with Monte Carlo tree search. The solution to the POMDP is a policy that performs high-level driving maneuvers, such as giving way to an approaching car, keeping a safe distance from the vehicle in front or merging into traffic.
  arXiv  Detail & Related papers  (2023-03-29T16:12:45Z)
• Scene Transformer: A unified multi-task model for behavior prediction and planning [42.758178896204036]
  We formulate a model for predicting the behavior of all agents jointly in real-world driving environments. Inspired by recent language modeling approaches, we use a masking strategy as the query to our model. We evaluate our approach on autonomous driving datasets for behavior prediction, and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-06-15T20:20:44Z)
• Diverse Critical Interaction Generation for Planning and Planner Evaluation [19.08679318138525]
  We propose a styled generative model RouteGAN that generates diverse interactions by controlling the vehicles separately with desired styles. By altering its style coefficients, the model can generate trajectories with different safety levels serve as an online planner.
  arXiv  Detail & Related papers  (2021-03-01T10:52:36Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)
• SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving [96.50297622371457]
  Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to interact with diverse road users in diverse scenarios remains largely unsolved. We develop a dedicated simulation platform called SMARTS that generates diverse and competent driving interactions.
  arXiv  Detail & Related papers  (2020-10-19T18:26:10Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.