Mathematical Models of Human Drivers Using Artificial Risk Fields

TL;DR

This paper introduces a novel approach using artificial risk fields to model and predict human driving behavior, demonstrating high accuracy in trajectory prediction within a realistic simulator environment.

Contribution

The paper presents a new stochastic modeling method based on risk fields for predicting human driver actions, validated with real driving data and convex optimization techniques.

Findings

Risk fields effectively predict driver trajectories up to 20 seconds ahead.

The convex optimization approach accurately infers risk fields from driving data.

Challenges remain in modeling acceleration and deceleration decisions.

Abstract

In this paper, we use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations. A risk field assigns a non-negative risk measure to the state of the system in order to model how close that state is to violating a safety property, such as hitting an obstacle or exiting the road. Using risk fields, we construct a stochastic model of the operator that maps from states to likely actions. We demonstrate our approach on a driving task wherein human subjects are asked to drive a car inside a realistic driving simulator while avoiding obstacles placed on the road. We show that the most likely risk field given the driving data is obtained by solving a convex optimization problem. Next, we apply the inferred risk fields to generate distinct driving behaviors while comparing predicted trajectories against ground truth measurements. We observe that the risk fields are excellent at predicting future trajectory distributions with high prediction accuracy for up to twenty seconds prediction horizons. At the same time, we observe some challenges such as the inability to account for how drivers choose to accelerate/decelerate based on the road conditions.