Abstract:

A variable following characteristic traffic vehicle modeling method for intelligent driving system testing is proposed in this paper. Firstly， by clustering and analyzing natural driving data， a highly realistic interactive personalized car following model is established， and the model output coupling is used to assign multiple weights to construct a traffic vehicle model with variable car following characteristics that can be used for intelligent driving system testing. Then， by establishing the traffic vehicle trajectory evaluation method， the rationality， diversity and authenticity of the model's output trajectory are verified. Finally， a joint simulation platform is built to test the application of the constructed traffic vehicle model to the Automatic Emergency Braking （AEB） algorithm. The results show that the traffic vehicle model constructed in this paper can output reasonable， diverse， and realistic trajectories under different car following characteristics. When the number of trajectories reaches 60 000， the average root mean square error matched with the real natural driving speed trajectory is 0.427 m/s. Moreover， the behavioral response of the tested system varies under different traffic vehicle trajectory characteristics. By changing the weight coefficients， the evolution law of the tested system response can be revealed， and targeted testing of the tested system performance can be achieved.

Key words: intelligent driving system testing, traffic vehicle modeling, variable car following characteristic, Transformer network, multiple weight allocation