PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario

• URL: http://arxiv.org/abs/2504.05908v1
• Date: Tue, 08 Apr 2025 11:06:02 GMT
• Title: PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario
• Authors: Sriram Mandalika, Lalitha V, Athira Nambiar,
• Abstract summary: PRIMEDrive-CoT is a novel uncertainty-aware model for object interaction and Chain-of-Thought (CoT) reasoning in driving scenarios.<n>Our approach combines LiDAR-based 3D object detection with multi-view RGB references to ensure interpretable and reliable scene understanding.
• Score: 1.1142444517901016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driving scene understanding is a critical real-world problem that involves interpreting and associating various elements of a driving environment, such as vehicles, pedestrians, and traffic signals. Despite advancements in autonomous driving, traditional pipelines rely on deterministic models that fail to capture the probabilistic nature and inherent uncertainty of real-world driving. To address this, we propose PRIMEDrive-CoT, a novel uncertainty-aware model for object interaction and Chain-of-Thought (CoT) reasoning in driving scenarios. In particular, our approach combines LiDAR-based 3D object detection with multi-view RGB references to ensure interpretable and reliable scene understanding. Uncertainty and risk assessment, along with object interactions, are modelled using Bayesian Graph Neural Networks (BGNNs) for probabilistic reasoning under ambiguous conditions. Interpretable decisions are facilitated through CoT reasoning, leveraging object dynamics and contextual cues, while Grad-CAM visualizations highlight attention regions. Extensive evaluations on the DriveCoT dataset demonstrate that PRIMEDrive-CoT outperforms state-of-the-art CoT and risk-aware models.

Related papers

• SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models [63.71984266104757]
  Multimodal Large Language Models (MLLMs) can process both visual and textual data.<n>We propose SafeAuto, a novel framework that enhances MLLM-based autonomous driving systems by incorporating both unstructured and structured knowledge.
  arXiv  Detail & Related papers  (2025-02-28T21:53:47Z)
• CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions [13.981748780317329]
  Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs) This study introduces a novel accident anticipation framework for AVs, termed CRASH. It seamlessly integrates five components: object detector, feature extractor, object-aware module, context-aware module, and multi-layer fusion. Our model surpasses existing top baselines in critical evaluation metrics like Average Precision (AP) and mean Time-To-Accident (mTTA)
  arXiv  Detail & Related papers  (2024-07-25T04:12:49Z)
• Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control [6.492311803411367]
  Advanced perception systems utilizing black-box Deep Neural Networks (DNNs) demonstrate human-like comprehension. Unpredictable behavior and lack of interpretability may hinder their deployment in safety critical scenarios.
  arXiv  Detail & Related papers  (2024-03-22T19:04:58Z)
• 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)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view. To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• 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)
• Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks [17.989423104706397]
  This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction framework. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. We employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings.
  arXiv  Detail & Related papers  (2023-09-05T06:28:13Z)
• 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)
• RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments [72.04891523115535]
  We propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals. The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.
  arXiv  Detail & Related papers  (2022-07-16T12:40:17Z)
• Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction [29.701029725302586]
  We study the joint trajectory prediction problem with the goal-conditioned framework. We introduce a conditional-variational-autoencoder-based (CVAE) model to explicitly encode different interaction modes into the latent space. We propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
  arXiv  Detail & Related papers  (2022-03-28T21:41:21Z)
• RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks [19.582873794287632]
  A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. We propose a novel driving framework for egocentric views based on traffic graphs. We claim that our framework learns risk-aware representations by improving on the task of risk object identification.
  arXiv  Detail & Related papers  (2020-11-17T15:49:22Z)

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.