From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving

• URL: http://arxiv.org/abs/2504.01408v1
• Date: Wed, 02 Apr 2025 06:48:50 GMT
• Title: From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving
• Authors: Korbinian Moller, Luis Schwarzmeier, Johannes Betz,
• Abstract summary: This research builds upon and extends existing approaches in risk-aware motion planning and occlusion tracking.<n>We enhance a phantom agent-centric model by incorporating sequential reasoning to track occluded areas and predict potential hazards.<n> Simulations demonstrate that the proposed approach improves situational awareness and balances proactive safety with efficient traffic flow.
• Score: 1.8434042562191815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous vehicles (AVs) must navigate dynamic urban environments where occlusions and perception limitations introduce significant uncertainties. This research builds upon and extends existing approaches in risk-aware motion planning and occlusion tracking to address these challenges. While prior studies have developed individual methods for occlusion tracking and risk assessment, a comprehensive method integrating these techniques has not been fully explored. We, therefore, enhance a phantom agent-centric model by incorporating sequential reasoning to track occluded areas and predict potential hazards. Our model enables realistic scenario representation and context-aware risk evaluation by modeling diverse phantom agents, each with distinct behavior profiles. Simulations demonstrate that the proposed approach improves situational awareness and balances proactive safety with efficient traffic flow. While these results underline the potential of our method, validation in real-world scenarios is necessary to confirm its feasibility and generalizability. By utilizing and advancing established methodologies, this work contributes to safer and more reliable AV planning in complex urban environments. To support further research, our method is available as open-source software at: https://github.com/TUM-AVS/OcclusionAwareMotionPlanning

Related papers

• INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation [7.362380225654904]
  INSIGHT is a hierarchical vision-language model (VLM) framework designed to enhance hazard detection and edge-case evaluation.<n>By using multimodal data fusion, our approach integrates semantic and visual representations, enabling precise interpretation of driving scenarios.<n> Experimental results on the BDD100K dataset demonstrate a substantial improvement in hazard prediction straightforwardness and accuracy over existing models.
  arXiv  Detail & Related papers  (2025-02-01T01:43:53Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation [5.928213664340974]
  This research addresses the safety issue in the control optimization problem of autonomous driving. We propose a novel, model-based approach for policy optimization, utilizing a conditional Value-at-Risk based Soft Actor Critic. Our method introduces a worst-case actor to guide safe exploration, ensuring rigorous adherence to safety requirements even in unpredictable scenarios.
  arXiv  Detail & Related papers  (2024-07-08T18:32:40Z)
• 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)
• Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis [63.532413807686524]
  This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL) We propose a new architecture that handles the trade-off between efficient progress and safety during exploration.
  arXiv  Detail & Related papers  (2023-12-18T16:09:43Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames [12.244501203346566]
  We develop a novel multi-agent dynamic occlusion risk measure for assessing situational risk. We present a white-box, scenario-based, accelerated safety validation framework for assessing safety of strategic planners in AV.
  arXiv  Detail & Related papers  (2021-09-20T19:38:14Z)
• Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems [34.945482759378734]
  We employ a probabilistic approach to safety evaluation in simulation, where we are concerned with computing the probability of dangerous events. We develop a novel rare-event simulation method that combines exploration, exploitation, and optimization techniques to find failure modes and estimate their rate of occurrence.
  arXiv  Detail & Related papers  (2020-08-24T17:46:27Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)
• Safe Mission Planning under Dynamical Uncertainties [15.533842336139063]
  This paper considers safe robot mission planning in uncertain dynamical environments. It is a challenging problem due to modeling and integrating dynamical uncertainties into a safe planning framework.
  arXiv  Detail & Related papers  (2020-03-05T20:45:42Z)

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.