Robust Traffic Light Detection Using Salience-Sensitive Loss:
  Computational Framework and Evaluations
Ross Greer Akshay Gopalkrishnan Jacob Landgren Lulua Rakla Anish Gopalan Mohan Trivedi
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.