Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment
Driver behavior is a critical factor in driving safety, making the development of sophisticated
distraction classification methods essential. Our study presents a Distracted Driving …
distraction classification methods essential. Our study presents a Distracted Driving …
Vision-language models can identify distracted driver behavior from naturalistic videos
Recognizing the activities causing distraction in real-world driving scenarios is critical for
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
ensuring the safety and reliability of both drivers and pedestrians on the roadways …
Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
Addressing hard cases in autonomous driving, such as anomalous road users, extreme
weather conditions, and complex traffic interactions, presents significant challenges. To …
weather conditions, and complex traffic interactions, presents significant challenges. To …
Enhancing the performance of a model to predict driving distraction with the random forest classifier
S Ahangari, M Jeihani, A Ardeshiri… - Transportation …, 2021 - journals.sagepub.com
Distracted driving is known to be one of the main causes of crashes in the United States,
accounting for about 40% of all crashes. Drivers' situational awareness, decision-making …
accounting for about 40% of all crashes. Drivers' situational awareness, decision-making …
A machine learning distracted driving prediction model
Distracted driving is known to be one of the core contributors to crashes in the US,
accounting for about 40% of all crashes. Drivers' situational awareness, decision-making …
accounting for about 40% of all crashes. Drivers' situational awareness, decision-making …
[HTML][HTML] LLM multimodal traffic accident forecasting
With the rise in traffic congestion in urban centers, predicting accidents has become
paramount for city planning and public safety. This work comprehensively studied the …
paramount for city planning and public safety. This work comprehensively studied the …
Hierarchical Automated Machine Learning Approach for Self-Optimizable Driving Distraction Recognition Based on Driver's Lane-Keeping Performance
C Chai, J Li, MM Islam, R Feng… - Transportation Research …, 2023 - journals.sagepub.com
With the enrichment of smartphone uses, phone-related driving distractions have become a
threat to driving safety. One way to mitigate driving distractions is to detect them and provide …
threat to driving safety. One way to mitigate driving distractions is to detect them and provide …
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 …
damage, have long been a focal point of research for many scholars in the field of traffic …
Detection of driver engagement in secondary tasks from observed naturalistic driving behavior
Distracted driving has long been acknowledged as one of the leading causes of death or
injury in roadway crashes. The focus of past research has been mainly on the impact of …
injury in roadway crashes. The focus of past research has been mainly on the impact of …
A survey of large language models for autonomous driving
Autonomous driving technology, a catalyst for revolutionizing transportation and urban
mobility, has the tend to transition from rule-based systems to data-driven strategies …
mobility, has the tend to transition from rule-based systems to data-driven strategies …