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. In various real-world driving situations, a scene may have numerous traffic lights with varying levels of relevance to the driver, and thus, distinguishing and detecting the lights that are relevant to the driver and influence the driver's actions is a critical safety task. This paper proposes a traffic light detection model which focuses on this task by first defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights Dataset, the first US traffic light dataset with an annotated salience property. Subsequently, we train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights, showing that a model trained with this loss function has stronger recall than one trained without.

(Safe) SMART Hands: Hand Activity Analysis and Distraction Alerts Using a Multi-Camera Framework

Manual (hand-related) activity is a significant source of crash risk while driving. Accordingly, analysis of hand position and hand activity occupation is a useful component to understanding a driver's readiness to take control of a vehicle. Visual sensing through cameras provides a passive means of observing the hands, but its effectiveness varies depending on camera location. We introduce an algorithmic framework, SMART Hands, for accurate hand classification with an ensemble of camera views using machine learning. We illustrate the effectiveness of this framework in a 4-camera setup, reaching 98% classification accuracy on a variety of locations and held objects for both of the driver's hands. We conclude that this multi-camera framework can be extended to additional tasks such as gaze and pose analysis, with further applications in driver and passenger safety.