Engineers from Loughborough University in the UK and Jaguar Land Rover have proposed a coordinated control architecture for motion management in advanced driver assistance systems (ADAS) to increase safety and comfort across all vehicles, regardless of ADAS specifics.
They published their proposal in an open-access paper in the IEEE/CAA Journal of Automatica Sinica, a joint publication of the IEEE and the Chinese Association of Automation.
Each of the various types of ADAS systems in service today generally provide a unique feature for the user that is implemented through additional control of one of the vehicle’s systems, e.g. braking or steering. ADAS systems must not be regarded as a substitute for drivers but rather as a co-driver, even if direct involvement in some of the driving tasks is not required.
The main contribution of this paper is a coordinated control architecture for vehicle motion management and simulation environment to facilitate ADAS development and testing, especially when details of the ADAS are not accessible.—Lin et al.
The physics models and control architecture are implemented in MATLAB/Simulink. These are openly available to the community for further research and development. The team anticipates that the architecture will not be susceptible to detail changes in the vehicle dynamics model.
The coordinated control architecture for the motion management consists of four levels: at the top, an ADAS level that generates ideal trajectory as commands; a motion co-ordinator at upper-level (task manager); coordinated dynamic controllers at lower-level; and a vehicle dynamics model at the bottom.
One of the most common ADAS alerts drivers when the car drifts out of their lane, or helps a driver intentionally change lanes. With that in mind, Lin and his team integrated physics models of vehicle dynamics and ADAS command models. They ran simulations on this combined information, and analyzed how control from different, independent vehicle systems might influence the vehicle trajectory as it changes lanes.
Upon receiving an ideal trajectory from the ADAS command, the task manager at the upper-level is based on the coordination management laws to assign different tasks to the two independent controllers. Each of the controllers will identify their own input signal to achieve the independent control scheme at the lower-level for the controlled objects, such as the steering system actuator and the brake system actuator. Then, these two system actuators will give the active steering angle to the two front wheels, and the corrective yaw moment to the brake system for the braking torque distribution to the physics based vehicle model. After the completion of the control process, the interaction between steering system and braking system can therefore be investigated and evaluated.—Lin et al.
They found that the steering system caused the simulated vehicle to undershoot, while the brake system overshot. On the other hand, the coordinated control strategy successfully damped out the deviation errors, and gave much greater precision in following the intended trajectory.
The researchers will continue to explore the system of systems control architecture to better develop coordinated control in ADAS. They will also examine how new systems interfere with existing systems to fully understand control performance and stability.
The work was supported by the Program for Simulation Innovation at Loughborough University.
Tzu-Chi Lin, Siyuan Ji, Charles E. Dickerson, David Battersby (2018) “Coordinated control architecture for motion management in ADAS systems” IEEE/CAA Journal of Automatica Sinica Volume: 5 Issue: 2 doi: 10.1109/JAS.2017.7510814