  Cycling is a healthy and sustainable mode of transport. However, interactions
with motor vehicles remain a key barrier to increased cycling participation.
The ability to detect potentially dangerous interactions from on-bike sensing
could provide important information to riders and policy makers. Thus,
automated detection of conflict between cyclists and drivers has attracted
researchers from both computer vision and road safety communities. In this
paper, we introduce a novel benchmark, called Cyc-CP, towards cycling close
pass near miss event detection from video streams. We first divide this task
into scene-level and instance-level problems. Scene-level detection asks an
algorithm to predict whether there is a close pass near miss event in the input
video clip. Instance-level detection aims to detect which vehicle in the scene
gives rise to a close pass near miss. We propose two benchmark models based on
deep learning techniques for these two problems. For training and testing those
models, we construct a synthetic dataset and also collect a real-world dataset.
Our models can achieve 88.13% and 84.60% accuracy on the real-world dataset,
respectively. We envision this benchmark as a test-bed to accelerate cycling
close pass near miss detection and facilitate interaction between the fields of
road safety, intelligent transportation systems and artificial intelligence.
Both the benchmark datasets and detection models will be available at
https://github.com/SustainableMobility/cyc-cp to facilitate experimental
reproducibility and encourage more in-depth research in the field.
