AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis
Traffic accident analysis is pivotal for enhancing public safety and developing road regulations. Traditional approaches, although widely used, are often constrained by manual analysis processes, subjective decisions, uni-modal outputs, as well as privacy issues related to sensitive data. This paper introduces the idea of AccidentGPT, a foundation model of traffic accident analysis, which incorporates multi-modal input data to automatically reconstruct the accident process video with dynamics details, and furthermore provide multi-task analysis with multi-modal outputs. The design of the AccidentGPT is empowered with a multi-modality prompt with feedback for task-oriented adaptability, a hybrid training schema to leverage labelled and unlabelled data, and a edge-cloud split configuration for data privacy. To fully realize the functionalities of this model, we proposes several research opportunities. This paper serves as the stepping stone to fill the gaps in traditional approaches of traffic accident analysis and attract the research community attention for automatic, objective, and privacy-preserving traffic accident analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
Privacy PreservingSimilar Papers 제목 키워드 기반
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model
Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, wheth…
Autonomous DrivingScene UnderstandingFOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
In order to navigate complex traffic environments, self-driving vehicles must recognize many semantic classes pertaining to vulnerable road users or traffic control devices. However, many safety-critical objects (e.g., c…
3D Object DetectionOpen-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation
With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technolo…
2D Object DetectionImage RetrievalRetrievalTowards Safe Mobility: A Unified Transportation Foundation Model enabled by Open-Ended Vision-Language Dataset
Urban transportation systems face growing safety challenges that require scalable intelligence for emerging smart mobility infrastructures. While recent advances in foundation models and large-scale multimodal datasets h…
Visual Question AnsweringAutonomous DrivingMTP: Exploring Multimodal Urban Traffic Profiling with Modality Augmentation and Spectrum Fusion
With rapid urbanization in the modern era, traffic signals from various sensors have been playing a significant role in monitoring the states of cities, which provides a strong foundation in ensuring safe travel, reducin…
Contrastive Learning