Real-time Prediction of Intermediate-Horizon Automotive Collision Risk

Understand

Advanced collision avoidance and driver hand-off systems can benefit from the ability to accurately predict, in real time, the probability a vehicle will be involved in a collision within an intermediate horizon of 10 to 20 seconds.

• The rarity of collisions in real-world data poses a significant challenge to developing this capability because, as we demonstrate empirically, intermediate-horizon risk prediction depends heavily on high-dimensional driver behavioral features.
• As a result, a large amount of data is required to fit an effective predictive model.
• In this paper, we assess whether simulated data can help alleviate this issue.