  Many automotive applications, such as Advanced Driver Assistance Systems
(ADAS) for collision avoidance and warnings, require estimating the future
automotive risk of a driving scene. We present a low-cost system that predicts
the collision risk over an intermediate time horizon from a monocular video
source, such as a dashboard-mounted camera. The modular system includes
components for object detection, object tracking, and state estimation. We
introduce solutions to the object tracking and distance estimation problems.
Advanced approaches to the other tasks are used to produce real-time
predictions of the automotive risk for the next 10 s at over 5 Hz. The system
is designed such that alternative components can be substituted with minimal
effort. It is demonstrated on common physical hardware, specifically an
off-the-shelf gaming laptop and a webcam. We extend the framework to support
absolute speed estimation and more advanced risk estimation techniques.
