Risk-Aware Planning: Methods and Case Study for Safer Driving Routes

John Krumm, Eric Horvitz

Abstract: Vehicle crashes account for over one million fatalities and many more million injuries annually worldwide. Some roads are safer than others, so a driving route optimized for safety may reduce the number of crashes. We have developed a method to estimate the probability of a crash on any road as a function of the traffic volume, road characteristics, and environmental conditions. We trained a regression model to estimate traffic volume and a binary classifier to estimate crash probability on road segments. Modeling a route’s crash probability as a series of Bernoulli probability trials, we show how to use a simple Dijkstra algorithm to compute the safest route between two locations. Compared to the fastest route, the safest route averages about 1.7 times as long in duration and about half as dangerous. We also show how to smoothly trade off safety for time, giving several different route options with different crash probabilities and durations.