Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Abstract

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

On the Benefits of Using Object-Oriented Programming for the Objective Evaluation of Vehicle Dynamic Performance in Concurrent Simulations

“…A new simulation environment was designed and implemented using modern C++ (ISO 17), although we were inspired by an existing simulator [34]. Thanks to the modern object-oriented programming (OOP) paradigm, the proposed simulator offers a modular infrastructure, where code procedures and data are hierarchically grouped and systematically encapsulated into objects [27].…”

“…Their complexity ranges from simple 2 DOF quarter-car or bicycle models, which can capture only the basic vertical and either the longitudinal or the lateral behaviour of a vehicle, up to 15-18 DOF full-vehicle models (FVMs) [30][31][32]. Offering a good compromise between predictive accuracy and computational efficiency, FVMs already proved their effectiveness in supporting the vehicle dynamic simulation for RT driver-in-the-loop (DiL) testing [33,34]. A human driver immersed into a virtual-reality scenario acts on a virtual vehicle, enabling the assessment both subjectively and objectively of the influence of multiple design choices related, but not limited to, VD performances.…”

“…The presented research originates from the need for simulating several independently controlled actors, while allowing to choose among several vehicle variants, with different types of models or different control strategies. This motivation led us to design a new simulation environment, which was deeply inspired from a predecessor simulator [34]. The new architecture relies on the benefits of the object-oriented programming paradigm and exploits a hierarchical evaluation of the vehicle sub-modules, which is also more representative of the hierarchical organization of the plethora of different ECUs used in modern cars .…”

Autonomous vehicle path tracking control considering the stability under lane change

“…Furthermore, in addition to studies investigating the ability of tracking the planned trajectory, in the field of path tracking and obstacles avoidance, vehicle stabilization at high speed is another topic arousing researchers’ interest. Chen et al 10 proposed an approach including upper layer of the model predictive controller generating a collision-free trajectory and lower layer determining the optimal steering angle based on the linear time-variant MPC to follow the replanning path. Associated with the thought of chassis integrated control, Zhang et al 11 applied the integration of active front steering (AFS) and differential braking control (DBC) in the path tracking of autonomous vehicle.…”

Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

“…A video-based vehicle embedded PID autonomous steering control test was proposed in Refs. [4,5]. Yaw rate tracking error based PID active front-wheel steering control was adopted to improve the vehicle steering dynamics, and an embedded control structure with two independent control loops was proposed.…”