Learning Human-Like Trajectory Planning on Urban Two-Lane Curved Roads From Experienced Drivers

Abstract

In the coming decades, it is a universal consensus that autonomous vehicles (AVs) and human-driven vehicles will share the traffic roads. Trajectory planning of AVs has been extensively studied from the perspective of driving safety and riding comfort. However, human-like trajectory planning has rarely been studied. In this paper, we characterize and model human driving trajectories using real vehicle field test data collected on five two-way and two-lane urban curved roads with 20 experienced drivers and 3 experimental vehicles. A differential global positioning system (GPS) and an inertial navigation system (INS) are used to measure the vehicle positions and velocities in high precision. We study the trajectory characteristics of experienced drivers on curved two-lane roads, especially the relationships between the vehicle trajectories on bidirectional two lanes. Based on long short-term memory neural network (LSTM NN), we develop a data-driven trajectory model to generate human-like driving trajectories. By comparing with other three modeling methods, the LSTM NN model was validated and tested in various cases with promising performance.