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

Event: CVPR 2025 · Duration: 4 min · ▶ Watch on YouTube

Abstract

In autonomous driving scenarios, traditional deterministic models often fail to capture the inherent probabilistic nature of real-world driving, especially in high-risk situations. This work introduces PRIMEDrive-CoT, a precognitive Chain-of-Thought (CoT) framework designed to address this by integrating uncertainty measurements with Bayesian models. The framework enables the system to generate interpretable explanations for complex driving actions like braking or lane changes, highlighting attention to relevant objects through complementary Grad-CAM visualizations. By modeling vehicle-to-vehicle and vehicle-to-pedestrian interactions using Bayesian Graph Neural Networks (BGNNs), PRIMEDrive-CoT facilitates probabilistic relational reasoning under ambiguous and high-risk scenarios, enhancing real-time safety and decision interpretability.

Speakers

  • Sriram Mandalika — Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India - 603203
  • Lalitha V — Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India - 603203
  • Athira Nambiar — Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India - 603203

Talks (1)

  • 00:00:00 — Sriram Mandalika: PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario
    • This paper introduces PRIMEDrive-CoT, a precognitive and interpretable Chain-of-Thought framework that integrates LiDAR and multi-view images for uncertainty-aware object interaction and risk-aware decision-making in autonomous driving.

Key Takeaways

  • Introduced PRIMEDrive-CoT, a precognitive and interpretable Chain-of-Thought (CoT) framework that integrates LiDAR and multi-view images for uncertainty-aware object interaction and risk-aware decision-making in autonomous driving.
  • Modeled vehicle-to-vehicle and vehicle-to-pedestrian interactions using Bayesian Graph Neural Networks (BGNNs), enabling probabilistic relational reasoning under ambiguous and high-risk scenarios.
  • Proposed a proximity-aware risk metric to prioritize high-risk objects based on spatial distance and uncertainty, enhancing real-time safety and decision interpretability.

Methods / Models / Datasets Mentioned

  • PRIMEDrive-CoT
  • Chain-of-Thought (CoT)
  • Bayesian models
  • Grad-CAM visualizations
  • Bayesian Graph Neural Network (BGNN)
  • MVX-Net
  • Baseline VoxelNet
  • Transfuser
  • TCP
  • Interfuser
  • DriveCoT-Agent

Topics

Autonomous driving · Chain-of-Thought (CoT) · Uncertainty-aware · Object interaction · Risk assessment · Bayesian Graph Neural Networks (BGNNs) · LiDAR · Multi-view images · Decision-making · Interpretability

Notes

Open for commentary — connections to other work, critiques, follow-up reading.