The selected motions of autonomous vehicles (AVs) are subject to the constraints from the surrounding traffic environment, infrastructure and the vehicle's dynamic capabilities. Normally, the motion control of the vehicle is composed of trajectory planning and trajectory following according to the surrounding risk factors, the vehicles’ capabilities as well as tyre/road interaction situations. However, pure trajectory following with a unique path may make the motion control of the vehicle be too careful and cautious with a large amount of steering effort. To follow a planned trajectory, the AVs with the traditional path-following control algorithms will correct their states even if the vehicles have only a slight deviation from the desired path or the vehicle detects static infrastructure like roadside trees. In this case, the safety of the AVs can be guaranteed to some degree, but the comfort and sense of hazards for the drivers are ignored, and sometimes the AVs have unusual motion behaviours which may not be acceptable to other road users. To solve this problem, this study aims to develop a safety corridor-based vehicle motion control approach by investigating human-driven vehicle behaviour and the vehicle's dynamic capabilities. The safety corridor is derived by the manoeuvring action feedback of actual drivers as collected in a driving simulator when presented with surrounding risk elements and enables the AVs to have safe trajectories within it. A corridor-based Nonlinear Model Predictive Control (NMPC) has been developed which controls the vehicle state to achieve a smooth and comfortable trajectory while applying trajectory constraints using the safety corridor. The safety corridor and motion controller are assessed using four typical scenarios to show that the vehicle has a human-like or human-oriented behaviour which is expected to be more acceptable for both drivers and other road users.
|Number of pages||14|
|Journal||Transportation Research Part C: Emerging Technologies|
|Early online date||13 Aug 2019|
|Publication status||Published - Oct 2019|
Bibliographical noteFunding Information:
The work reported here is supported by the HumanDrive project, funded by the UK’s Centre for Connected and Automated Vehicles (CCAV) and Innovate UK (Grant number TS/P012035/1 ). The authors gratefully acknowledge the contribution of Andrew Tomlinson, Anthony Horrobin, and Michael Daly for to the preparation of the simulated driving environment and preparation of the data for analysis.
© 2019 Elsevier Ltd
Copyright 2019 Elsevier B.V., All rights reserved.
- Autonomous vehicles
- Model predictive control
- Risk-based corridor
ASJC Scopus subject areas
- Civil and Structural Engineering
- Automotive Engineering
- Computer Science Applications