Search Results for author:
Found 8 papers, 2 papers with code
Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving
Vision-Language Models (VLMs) and Multi-Modal Language models (MMLMs) have become prominent in autonomous driving research, as these models can provide interpretable textual reasoning and responses for end-to-end autonomous driving safety tasks using traffic scene images and other data modalities.
Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol
About 30% of all traffic crash fatalities in the United States involve drunk drivers, making the prevention of drunk driving paramount to vehicle safety in the US and other locations which have a high prevalence of driving while under the influence of alcohol.
Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations
Vehicle light detection, association, and localization are required for important downstream safe autonomous driving tasks, such as predicting a vehicle's light state to determine if the vehicle is making a lane change or turning.
Patterns of Vehicle Lights: Addressing Complexities in Curation and Annotation of Camera-Based Vehicle Light Datasets and Metrics
Overall, this paper provides insights into the representation of vehicle lights and the importance of accurate annotations for training effective detection models in autonomous driving applications.
Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations
One of the most important tasks for ensuring safe autonomous driving systems is accurately detecting road traffic lights and accurately determining how they impact the driver's actions.
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis
It is critical for vehicles to prevent any collisions with pedestrians.
CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems
Moreover, we use the number of correct advisories, false advisories, and missed advisories to define precision and recall performance metrics to evaluate CHAMP.
Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context
Next, we use a custom salience loss function, Salience-Sensitive Focal Loss, to train a Deformable DETR object detection model in order to emphasize stronger performance on salient signs.
