Real-time prediction of intermediate-horizon automotive collision risk
Advanced collision avoidance and driver hand-off systems can benefit from the ability to
accurately predict, in real time, the probability a vehicle will be involved in a collision within …
accurately predict, in real time, the probability a vehicle will be involved in a collision within …
Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving
AJM Muzahid, SF Kamarulzaman… - 2020 Emerging …, 2020 - ieeexplore.ieee.org
The autonomous driving is increasingly mounting, promoting, and promising the future of
fully autonomous and, correspondingly presenting new challenges in the field of safety …
fully autonomous and, correspondingly presenting new challenges in the field of safety …
Calibrated confidence learning for large-scale real-time crash and severity prediction
Real-time crash and severity prediction is a complex task, and there is no existing framework
to predict crash likelihood and severity together. Creating such a framework poses …
to predict crash likelihood and severity together. Creating such a framework poses …
Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving
Large Language Models (LLMs) have garnered significant attention for their ability to
understand text and images, generate human-like text, and perform complex reasoning …
understand text and images, generate human-like text, and perform complex reasoning …
[HTML][HTML] A hybrid Bayesian Network approach to detect driver cognitive distraction
Driver cognitive distraction (eg, hand-free cell phone conversation) can lead to unapparent,
but detrimental, impairment to driving safety. Detecting cognitive distraction represents an …
but detrimental, impairment to driving safety. Detecting cognitive distraction represents an …
Modeling driver's evasive behavior during safety–critical lane changes: Two-dimensional time-to-collision and deep reinforcement learning
Lane changes are complex driving behaviors and frequently involve safety–critical
situations. This study aims to develop a lane-change-related evasive behavior model, which …
situations. This study aims to develop a lane-change-related evasive behavior model, which …
RsSafe: Personalized driver behavior prediction for safe driving
While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc.
provides livelihood to many drivers, the desire to raise income forces the drivers to work very …
provides livelihood to many drivers, the desire to raise income forces the drivers to work very …
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
MM Hussien, AN Melo, AL Ballardini… - arXiv preprint arXiv …, 2024 - arxiv.org
Prediction of road users' behaviors in the context of autonomous driving has gained
considerable attention by the scientific community in the last years. Most works focus on …
considerable attention by the scientific community in the last years. Most works focus on …
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language Models
Large language models (LLMs) has been effectively used for many computer vision tasks,
including image classification. In this paper, we present a simple yet effective approach for …
including image classification. In this paper, we present a simple yet effective approach for …
Applying few-shot learning in classifying pedestrian crash typing
Pedestrian deaths account for 23% of all road traffic fatalities worldwide. After declining for
three decades, pedestrian fatalities in the United States have been increasing with 6,941 …
three decades, pedestrian fatalities in the United States have been increasing with 6,941 …