Control-ITRA: Controlling the Behavior of a Driving Model

• URL: http://arxiv.org/abs/2501.12408v1
• Date: Fri, 17 Jan 2025 03:35:11 GMT
• Title: Control-ITRA: Controlling the Behavior of a Driving Model
• Authors: Vasileios Lioutas, Adam Scibior, Matthew Niedoba, Berend Zwartsenberg, Frank Wood,
• Abstract summary: We introduce a method called Control-ITRA to influence agent behavior through waypoint assignment and target speed modulation. We demonstrate that our method can generate controllable, infraction-free trajectories while preserving realism in both seen and unseen locations.
• Score: 14.31198056147624
• License:
• Abstract: Simulating realistic driving behavior is crucial for developing and testing autonomous systems in complex traffic environments. Equally important is the ability to control the behavior of simulated agents to tailor scenarios to specific research needs and safety considerations. This paper extends the general-purpose multi-agent driving behavior model ITRA (Scibior et al., 2021), by introducing a method called Control-ITRA to influence agent behavior through waypoint assignment and target speed modulation. By conditioning agents on these two aspects, we provide a mechanism for them to adhere to specific trajectories and indirectly adjust their aggressiveness. We compare different approaches for integrating these conditions during training and demonstrate that our method can generate controllable, infraction-free trajectories while preserving realism in both seen and unseen locations.

Related papers

• Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors [2.773055342671194]
  We introduce a natural adversarial scenario generation solution using naturalistic human driving priors and reinforcement learning techniques. Our findings demonstrate that the proposed model can generate realistic safety-critical test scenarios covering both naturalness and adversariality.
  arXiv  Detail & Related papers  (2024-08-06T13:58:56Z)
• CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning [38.63187494867502]
  CtRL-Sim is a method that leverages return-conditioned offline reinforcement learning (RL) to efficiently generate reactive and controllable traffic agents. We show that CtRL-Sim can generate realistic safety-critical scenarios while providing fine-grained control over agent behaviours.
  arXiv  Detail & Related papers  (2024-03-29T02:10:19Z)
• 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)
• DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
  Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. We introduce DARTH, a holistic test-time adaptation framework for MOT.
  arXiv  Detail & Related papers  (2023-10-03T10:10:42Z)
• Model-Based Reinforcement Learning with Isolated Imaginations [61.67183143982074]
  We propose Iso-Dream++, a model-based reinforcement learning approach. We perform policy optimization based on the decoupled latent imaginations. This enables long-horizon visuomotor control tasks to benefit from isolating mixed dynamics sources in the wild.
  arXiv  Detail & Related papers  (2023-03-27T02:55:56Z)
• Guided Conditional Diffusion for Controllable Traffic Simulation [42.198185904248994]
  Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Data-driven approaches generate realistic and human-like behaviors, improving transfer from simulated to real-world traffic. We develop a conditional diffusion model for controllable traffic generation (CTG) that allows users to control desired properties of trajectories at test time.
  arXiv  Detail & Related papers  (2022-10-31T14:44:59Z)
• Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models [65.97707691164558]
  We present Iso-Dream, which improves the Dream-to-Control framework in two aspects. First, by optimizing inverse dynamics, we encourage world model to learn controllable and noncontrollable sources. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model.
  arXiv  Detail & Related papers  (2022-05-27T08:07:39Z)
• Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
  We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
  arXiv  Detail & Related papers  (2022-04-28T07:37:21Z)
• Towards autonomous artificial agents with an active self: modeling sense of control in situated action [0.0]
  We present a computational modeling account of an active self in artificial agents. We focus on how an agent can be equipped with a sense of control and how it arises in autonomous situated action.
  arXiv  Detail & Related papers  (2021-12-10T14:45:24Z)

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.