Using light-weight architectures or reasoning on low-resolution images, recent methods realize very fast scene parsing, even running at more than 100 FPS on a single GPU. However, there is still a significant gap in performance between these real-time methods and the models based on dilation backbones. To this end, we proposed a family of deep dual-resolution networks (DDRNets) for real-time and accurate semantic segmentation, which consist of deep dual-resolution backbones and enhanced low-resolution contextual information extractors. The two deep branches and multiple bilateral fusions of backbones generate higher quality details compared to existing two-pathway methods. The enhanced contextual information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) enlarges effective receptive fields and fuses multi-scale context based on low-resolution feature maps with little time cost. Our method achieves a new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset. For the input of full resolution, on a single 2080Ti GPU without hardware acceleration, DDRNet-23-slim yields 77.4 $\%$ mIoU at 102 FPS on Cityscapes test set and 74.7 $\%$ mIoU at 230 FPS on CamVid test set. With widely used test augmentation, our method is superior to most state-of-the-art models and requires much less computation. Codes and trained models are available at https://github.com/ydhongHIT/DDRNet.

In this paper, an output feedback finite-time control method is investigated for stabilizing the perturbed vehicle active suspension system to improve the suspension performance. Since the physical suspension systems always exist the phenomenon of uncertainty or external disturbance, a novel disturbance compensator with finite-time convergence performance is proposed for efficiently compensating the unknown external disturbance. Moreover, the presented compensator is advantageous over the existing ones since it is continuous and can completely remove the matched disturbance. And from the viewpoint of practical implementation, continuous control law will not lead to chattering, which is desirable for electrical and mechanical systems. For the nominal suspension system without disturbance, a homogeneous controller with a simple filter is constructed to achieve a finite-time convergence property, where the filter is applied to obtain the unknown velocity signal. Thus, the nominal controller combines a disturbance compensator into an overall continuous control law, which provides two independent parts with a separate design unite and a highly flexibility for selecting the control gains. According to the geometric homogeneity and finite-time separation principle, it can be shown that the active suspension is finite-time stabilized. A designed example is given to illustrate the effectiveness of the presented controller for improving the vehicle ride performance.

This paper investigates the energy-efficiency design of adaptive control for active suspension systems with a bioinspired nonlinearity approach. To this aim, a bioinspired dynamics-based adaptive tracking control is proposed for nonlinear suspension systems. In many existing techniques, one important effort is used for canceling vibration energy transmitted by suspension inherent nonlinearity to improve ride comfort. Unlike existing methods, the proposed approach takes full advantage of beneficial nonlinear stiffness and damping characteristics inspired by the limb motion dynamics of biological systems to achieve advantageous nonlinear suspension properties with potentially less energy consumption. The stability analysis of the desired bioinspired nonlinear dynamics is conducted within the Lyapunov framework. Theoretical analysis and simulation results reveal that the proposed bioinspired nonlinear dynamics-based adaptive controller has a significant impact on the amount of energy consumption, considering the same basic control method and random excitation of road irregularity for a similar ride comfort performance.

This article proposes a motion planning approach for autonomous electric vehicles to generate an appropriate planned path according to the time-varying surrounding information. This approach utilizes the proposed novel sine resistance network to mesh the road with the aim of improving the planned path smoothness, which has the capability of generating a continuous-curvature planned path that contributes to tracking and reducing the jerkiness. Meanwhile, considering that the classical artificial potential field (APF) method is only suitable for the static scenarios, a bias oval APF is constructed to predict the change of relative distance between the ego vehicle and each obstacle by taking the speed information into account. The proposed planning approach can ensure that the planned path is collision-free in dynamic environments and the generated path is smooth simultaneously. Cosimulation results in CarSim and MATLAB/Simulink are provided to prove the advantage and feasibility of the proposed motion planning approach for autonomous electric vehicles.

In this paper, an adaptive fault-tolerant method is proposed to synthesize a compensation controller for the uncertain nonlinear pure-feedback systems possessing dead-zone actuators and stochastic failures. Each actuator's failure mode is described by a scalar Markovian type function, which is not only much more practical in control engineering but also challenging in control theory. By exploring the adaptive backstepping methodology, a compensation scheme for actuator failure is presented to guarantee that the solution of the closed-loop system is a unique and bounded in probability. Importantly, the proposed control method can achieve arbitrarily small tracking error in the presence of nonlinear actuators with random failures. The case study of active suspension system using the adaptive fault-tolerant compensation controller shows the effectiveness of the presented method.

In this article, an adaptive event-triggered fault-tolerant asymptotic tracking control problem guaranteeing prescribed performance is addressed for a class of block-triangular multi-input and multioutput uncertain nonlinear systems with unknown nonlinearities, unknown control directions, and actuator faults. Through a systematic co-design of the adaptive control law and the event-triggered mechanism, including fixed and relative threshold strategies, a control scheme with low structure and calculation complexity is designed to conserve system communication and computation resources. In this design, the output asymptotic tracking is achieved. The Nussbaum gain technique is incorporated to overcome unknown control directions with a new adaptive law, and a type of barrier Lyapunov function is adopted to handle the prescribed performance control problem, which contributes to a novel control law with strong robustness. The robust controller can address the uncertainties and couplings derived from the system structure, actuator faults, and event-triggered rules, without using approximating structures or compensators. Besides, the explosion of complexity is avoided. It is proved that all signals of the closed-loop system remain bounded, and system tracking errors asymptotically approach 0 with the prescribed performance, while the Zeno behavior is prevented. Finally, the effectiveness of the proposed control scheme is evaluated via an application example of the half-car active suspension system.