Anti-lock braking system (ABS) is one of the most effective active safety control systems for ground vehicles, since it can keep the rotational wheel from locking, and consequently guarantee the braking safety and handling stability. There have been a variety of ABS control schemes proposed by many researchers. However, most results employ sundry tire-road friction models, the alleged $\mu\ - \lambda $ curves (μ is tire-road friction coefficient, while λ is tire slip ratio, which is mathematically defined with equation (4)), making the ABS controller extremely complicated for the highly non-linear characteristics of $\mu\ - \lambda $ relationship. Furthermore, the priori knowledge of road conditions for these ABS controllers restricts their practicability. To circumvent these problems, a two-time-scale ABS control scheme is proposed in this paper, without considering the intricate $\mu\ - \lambda $ relationship, making the priori knowledge of road condition no longer a prerequisite, thus the designed ABS controller is rather simple. More than that, a modified fast-time-scale estimator is involved to estimate the road condition, which is significant in vehicle active dynamics control. The effectiveness of the proposed ABS controller is verified via numerical simulations and CarSim-Matlab co-simulations.

In this brief, a novel robust control of an antilock braking system (ABS) with the assistance of an active suspension system (ASS) is proposed for automobile to improve the braking performance. Different from other ABS, not the reference wheel slip ratio but the peak tire-road adhesion coefficient is taken as the control object, and the barrier Lyapunov function control method is adopted to confine the control object within a smaller and more stable area. Besides, the rolling resistance is taken as an external disturbance and counteracted by a modified controller. Furthermore, the increase of a tire vertical load via the ASS is explored to further shorten the braking distance under the condition that the trajectory of the sprung mass and suspension space is bounded to a predetermined limitation. Finally, a favorable control simulation consequence is obtained, validating the effectiveness of the proposed control scheme.

Active safety control is significant for ensuring the safety of both passengers and vehicles, and has been developed into various independent systems, such as anti-lock braking system (ABS), electronic stability programme (ESP) system, steering by wire (SbW) system, etc.. However, with these independent systems, the control targets could intervene with each other. As a feasible strategy, the coordinated control, also namely the bottom-to-top control scheme, can improve the overall vehicle performance to some extent, but the performance compromises among independent controllers and the redundancies of sensor harness limit their full function display. In order to reach the maximum performance improvement, the top-to-bottom integrated motion control scheme for 4-wheel-independent vehicles considering critical conditions is developed in this paper, that is, the vehicle motion is controlled with a virtual control input on a top level, and then the virtual control input is optimally allocated to and implemented by each wheel at the bottom level. The complicated allocation problem is handled via choosing proper allocation variables and corresponding allocation constraints, and meanwhile the critical working conditions which could be created by emergency obstacle avoidance, cornering braking manoeuvre, etc. are considered to guarantee the vehicle stability. The effectiveness of the proposed controller is verified via simulations with different driving conditions

Active air spring suspensions can improve the vehicle ride comfort and meanwhile realize the vehicle height regulation, and therefore, they have been widely used and studied. However, to achieve better ride comfort and a satisfactory vehicle body height adjustment, the active air suspension controller becomes an indispensable and significant part of the system. Since the nonlinear suspension system possesses uncertainties, it is difficult to take into account both ride comfort and height regulation. This study innovatively proposes an adaptive control algorithm to specifically address the problem of vehicle height regulation and ride comfort for nonlinear active air suspension systems with uncertainties. The accurate tracking to reference vehicle body height curves is realized, and the ride comfort is also improved. Through simulations with two scenarios, it is illustrated that the active air suspension controller owns better control effectiveness than the PID controller. Compared with the PID controller, the designed controller can track the reference vehicle body height curves faster and more accurately. The result also verifies the priority of the designed controller.

The visual perception model is critical to autonomous driving systems. It provides the information necessary for self-driving cars to make decisions in traffic scenes. We propose a lightweight multi-task network (Mobip) to simultaneously perform traffic object detection, drivable area segmentation, and lane line detection. The network consists of a shared encoder for feature extraction and two decoders for handling detection and segmentation tasks collectively. By using MobileNetV2 as the backbone and an extremely efficient multi-task architecture to implement the perception model, our network has great advantages in inference speed. The performance of the multi-task network is verified on a challenging public Berkeley Deep Drive(BDD100K) dataset. The model achieves an inference speed of 58 FPS on NVIDIA Tesla V100 while still maintaining competitive performance on all three tasks compared to other multi-task networks. Besides, the effectiveness and efficiency of the multi-task architecture are verified via ablative studies.