Iterative learning of an unknown road path through cooperative driving of vehicles

Abstract

This study proposes a method for a vehicle controller to learn human driving behaviours through iterative interactions. In particular, the vehicle controller and the human driver jointly control a vehicle along a path only known to the human driver. Through repeated cooperative driving, the vehicle controller estimates the hidden desired path of the driver by minimising the control input. Eventually, semi‐autonomous driving is realised since the vehicle controller is able to automatically track the target path and release the human driver from the driving task. The iterative learning of the human target path on the basis of the proposed algorithm is in the spatial domain, and is effective in the presence of uncertain human driving speeds. The validity of the proposed method is proved by rigorous analysis and demonstrated by numerical simulations.

Robust Adaptive Learning-Based Path Tracking Control of Autonomous Vehicles Under Uncertain Driving Environments

“…In the past years, due to its appealing features and ease of implementation, ILC has been applied in many areas including autonomous driving. For instance, [36] develops a spatial ILC method for semi-autonomous driving, which enables iterative learning of human driving behaviours to achieve satisfactory tracking performance. A learning control algorithm is proposed for path following task of an AV in [37] to deal with predefined periodic trajectories with unknown periods.…”

Robust learning‐based lateral tracking control for autonomous driving with input constraints

“…Moreover, in contrast to above mentioned model‐free and learning‐based control approaches, the implementation of ILC is more simple with fewer parameters to be tuned. Recently, due to the appealing learning ability of ILC and the repeatable operating environments of AVs, ILC has been introduced to path tracking control of AVs in References 29‐32. However, most of these works focus on linearized vehicle models, which may lead to huge deviations in practical driving, and thus result in unsatisfactory control performance.…”