Towards Safer and Understandable Driver Intention Prediction

• URL: http://arxiv.org/abs/2510.09200v1
• Date: Fri, 10 Oct 2025 09:41:25 GMT
• Title: Towards Safer and Understandable Driver Intention Prediction
• Authors: Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nandan Rai, Carlo Masone, C V Jawahar,
• Abstract summary: We introduce the task of interpretability in maneuver prediction before they occur for driver safety.<n>To foster research in interpretable DIP, we curate the DAAD-X, a new multimodal, ego-centric video dataset.<n>Next, we propose Video Concept Bottleneck Model (VCBM), a framework that generates coherent explanations inherently.
• Score: 30.136400523083907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomous systems and humans increase, the interpretability of decision-making processes in driving systems becomes increasingly crucial for ensuring safe driving operations. Successful human-machine interaction requires understanding the underlying representations of the environment and the driving task, which remains a significant challenge in deep learning-based systems. To address this, we introduce the task of interpretability in maneuver prediction before they occur for driver safety, i.e., driver intent prediction (DIP), which plays a critical role in AD systems. To foster research in interpretable DIP, we curate the eXplainable Driving Action Anticipation Dataset (DAAD-X), a new multimodal, ego-centric video dataset to provide hierarchical, high-level textual explanations as causal reasoning for the driver's decisions. These explanations are derived from both the driver's eye-gaze and the ego-vehicle's perspective. Next, we propose Video Concept Bottleneck Model (VCBM), a framework that generates spatio-temporally coherent explanations inherently, without relying on post-hoc techniques. Finally, through extensive evaluations of the proposed VCBM on the DAAD-X dataset, we demonstrate that transformer-based models exhibit greater interpretability than conventional CNN-based models. Additionally, we introduce a multilabel t-SNE visualization technique to illustrate the disentanglement and causal correlation among multiple explanations. Our data, code and models are available at: https://mukil07.github.io/VCBM.github.io/

Related papers

• ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving [64.12414815634847]
  Vision-Language Models (VLMs) and Driving World Models (DWMs) have independently emerged as powerful recipes addressing different aspects of this challenge.<n>We propose ImagiDrive, a novel end-to-end autonomous driving framework that integrates a VLM-based driving agent with a DWM-based scene imaginer.
  arXiv  Detail & Related papers  (2025-08-15T12:06:55Z)
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving [49.07731497951963]
  ReCogDrive is a novel Reinforced Cognitive framework for end-to-end autonomous driving.<n>We introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers.<n>We then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner.
  arXiv  Detail & Related papers  (2025-06-09T03:14:04Z)
• The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey [50.62538723793247]
  Driving World Model (DWM) focuses on predicting scene evolution during the driving process.<n>DWM methods enable autonomous driving systems to better perceive, understand, and interact with dynamic driving environments.
  arXiv  Detail & Related papers  (2025-02-14T18:43:15Z)
• VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision [17.36342349850825]
  Vision-language models (VLMs) as teachers to enhance training by providing additional supervision.<n>VLM-AD achieves significant improvements in planning accuracy and reduced collision rates on the nuScenes dataset.
  arXiv  Detail & Related papers  (2024-12-19T01:53:36Z)
• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• On depth prediction for autonomous driving using self-supervised learning [0.0]
  This thesis focuses on the challenge of depth prediction using monocular self-supervised learning techniques. The problem is approached from a broader perspective, exploring conditional generative adversarial networks (cGANs) The second contribution entails a single image-to-depth self-supervised method, proposing a solution for the rigid-scene assumption. The third significant aspect involves the introduction of a video-to-depth map forecasting approach.
  arXiv  Detail & Related papers  (2024-03-10T12:33:12Z)
• RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model [22.25903116720301]
  explainability plays a critical role in trustworthy autonomous decision-making. Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent. We present RAG-Driver, a novel retrieval-augmented multi-modal large language model that leverages in-context learning for high-performance, explainable, and generalisable autonomous driving.
  arXiv  Detail & Related papers  (2024-02-16T16:57:18Z)
• Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving [38.28159034562901]
  Reason2Drive is a benchmark dataset with over 600K video-text pairs. We characterize the autonomous driving process as a sequential combination of perception, prediction, and reasoning steps. We introduce a novel aggregated evaluation metric to assess chain-based reasoning performance in autonomous systems.
  arXiv  Detail & Related papers  (2023-12-06T18:32:33Z)
• On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving [37.617793990547625]
  This report provides an exhaustive evaluation of the latest state-of-the-art VLM, GPT-4V. We explore the model's abilities to understand and reason about driving scenes, make decisions, and ultimately act in the capacity of a driver. Our findings reveal that GPT-4V demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems.
  arXiv  Detail & Related papers  (2023-11-09T12:58:37Z)
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
  Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers. In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
  arXiv  Detail & Related papers  (2023-11-02T07:23:33Z)
• Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models [114.69732301904419]
  We present an approach to apply end-to-end open-set (any environment/scene) autonomous driving that is capable of providing driving decisions from representations queryable by image and text. Our approach demonstrates unparalleled results in diverse tests while achieving significantly greater robustness in out-of-distribution situations.
  arXiv  Detail & Related papers  (2023-10-26T17:56:35Z)

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.