Estimation method of risk potential parameters of traffic obstacles using vehicle state quantity

Abstract

In the risk potential driver model used to generate path control targets for autonomous vehicles, traffic elements such as obstacles and traffic rules are represented by risk potential, and safe routes are determined by synthesizing them. Therefore, it is possible to calculate the control target path adapted to the individual by quantifying the feel of risk that the human driver perceives and using it as a parameter of the driver model. This paper describes a method for estimating risk potential parameters using human driving maneuver. First, the travel path was functionally approximated to create a smooth trajectory similar to a skilled driver, and the steering operation to obtain this trajectory was calculated. Next, using the proposed method, the risk potential parameters were estimated using the nonlinear least squares method. It was confirmed that the driver model to which the estimation results were applied can reproduce the driving behavior of a human driver with high accuracy.