Experiment of aggressiveness estimation
In order to obtain the personalized parameters in (6), a series of experiments are conducted. All the experiments in this work are carried out using a 64-bit Windows 10 machine with an Intel Core i7 CPU, 32 GB of memory installed, and two Logitech G29 for driver input. The experiment environment is based on Simulink and Unreal engine with 10Hz sampling rate. Aggressiveness is an abstract idea and is difficult to quantify. Nevertheless, what we can obtain from the experiment is the extremity of aggressiveness, i.e., aggressiveness = 1. Hence, this experiment is designed to find the participant's limitations and then fit it into (6). In the experiment, the participant can observe the subject and surrounding vehicle, but she or he has no control over the subject vehicle except a stop button to terminate the experiment (details can be found in Note S8, Supporting Information). The subject vehicle and the obstacle vehicle initiated with a constant steering angle and velocity, which are designed to ensure a collision if the experiment is not stopped by the participant. The participant is asked to stop when she or he thinks the obstacle is too aggressive to undertake. When she or he stops, we record the current relative position and yaw angle. The subject vehicle is controlled by a PID controller with white Gaussian noise to enrich the dataset. After 5 repeated sets of experiments, the vehicle starts at a new position as in Figure \ref{481784}. There are a total of 6 different angles for collision. After that, we change the speed for another set of experiments. The obstacle maintains constant velocity and yaw angle. There are a total of 5 different velocity references. The experiment results and the fitting algorithm can be found in Note S5, Supporting Information. Meanwhile, comparisons of fitting results can also be found in Note S5, Supporting Information.