This article designs a jointly distributed adaptive sliding-mode controller (DASMC) and disturbance observer (DO) for a heterogeneous platoon of connected vehicles (CVs) subject to disturbance and acceleration failure of neighboring vehicles under the multifold common communication topologies. To circumvent the effects of disturbance and acceleration failure of neighboring vehicles, the disturbance arising from the vehicle heterogeneity and nonlinearity as well as the acceleration of neighboring vehicles is jointly estimated as a lumped disturbance by a DO. In particular, the sliding-mode surface with adaptive law is designed based on the defined lumped errors to support the diversity of communication topologies. Then, the DASMC with DO is designed according to the vehicle dynamic model. Meanwhile, the stability of the designed DASMC with DO is analyzed based on the Lyapunov technique. In addition, the string stability of the CV platoon is proved. Finally, the capabilities of the developed control scheme are demonstrated by the simulation and experiment under different communication topologies and compared with existing methods.

In recent years, the advancement of deep-learning technologies has greatly promoted the research progress of autonomous driving. However, deep neural network is like a black box. Given a specific input, it is difficult to explain the output of the network. Without explainable results, it would be unsafe to deploy deep networks in unseen environments or environments with potential unexpected situations. Especially for decision-making networks, inappropriate outputs could lead to severe traffic accidents. To provide a solution to this problem, we propose a deep neural network that jointly predicts the decision-making actions and corresponding natural-language explanations based on semantic scene understanding. Two types of explanations, the reasons of driving actions and the surrounding environment descriptions of the ego-vehicle, are designed. Both the reasons and descriptions are in the form of natural language. The decision-making actions could be explained by the corresponding reasons or the environment descriptions. We also release a large-scale dataset with hand-labelled ground truth including driving actions and environment descriptions. The superiority of our network over other methods is demonstrated on both our dataset and a public dataset.

Predicting pedestrian trajectories is crucial for human-interactive systems. This task is compounded by the inherently multimodal nature of human motions and complex external and internal factors such as social interactions and intentions. In this letter, we propose a dynamic attention-based CVAE-GAN method that simultaneously models time-varying social interactions and human intentions while generating multimodal trajectory predictions. Herein, CVAE is used to account for the multimodality of trajectories explicitly, and GAN is used for adversarial training. A spatio-temporal graph with dynamic attention is leveraged to encode pedestrian interactions, which are further fed into a recurrent neural network to capture the temporal dependencies of evolving patterns. Moreover, we aggregate all potential sub-goals from current position to the destination with another dynamic attention module to shape the predicted trajectories. Experiments on widely used ETH and UCY benchmarks will verify the effectiveness of the proposed method. In particular, the proposed method could outperform the state-of-the-art by approximately 10% in terms of prediction errors and 40% considering collision avoidance.

The connected automated vehicles (CAVs) are envisioned to be implemented most likely on electric vehicles, while traditional fuel-powered manually-driven vehicles (MVs) would probably still dominate the automobile market in the next decade. In this context, this paper addresses urban eco-driving of CAVs in mixed traffic and heterogeneous power conditions. The paper aims to develop a practical and deployable eco-driving strategy for CAVs in mixed traffic flow of CAVs and MVs under realistic and complex traffic conditions. Several typical eco-driving scenarios were studied in detail. In a nutshell, the eco-driving strategy for each CAV was determined by solving a typical two-point boundary value problem with minimum electric energy consumption in urban traffic conditions with small market penetration rates (MPRs) of CAVs. A rolling-horizon scheme was applied to implement the eco-driving strategy to handle uncertain/unpredictable disturbances of preceding MVs and the interference of junction queues to the eco-driving maneuvers of CAVs. The paper also studied how eco-driving for electrified CAVs would affect MVs’ fuel consumptions. Simulation studies were carried out on urban arterial roads of multiple signalized intersections in various scenarios of demand and MPR to verify the energy savings effect of the proposed eco-driving strategy. The results showed that via eco-driving electrified CAVs each had a potential of reducing energy consumption by 40%-61%, meanwhile leading to 5%-34% fuel savings on average for each following MV. Further issues concerning the energy saving mechanism of electrified CAVs, impacts of MVs cut-in from adjacent lanes, and passenger comfort were also examined.

This article proposes a finite-time based sliding-mode controller (FTSMC) and disturbance observer (FTDO) for connected vehicle (CV) platoon with uncertain dynamics. In particular, a recursive structure consisting of first-level and second-level sliding mode surfaces (SMSs) is developed for the chattering of the conventional SMC. Herein, the first-level SMS is designed based on a proportional-integral-derivative SMS considering the spacing error and interactive behaviors of vehicles, and the second-level SMS is based on an integral terminal SMS. Simultaneously, the disturbances suffered from the ego-vehicle uncertainty and nonlinearity are estimated by the FTDO. The FTSMC and FTDO are proposed to regulate the CV platoon in finite time. Meanwhile, the CV platoon can ensure the finite stability and string stability via rigorous analysis. Finally, the feasibility of the proposed controller is verified by extensive simulations and co-simulations, and small-scaled experiments.

This article develops a hierarchical control strategy for connected vehicle (CV) platoon. To this end, the article first characterizes the communication connectivity between CVs using the predecessor-leader following (PLF) topology. Then, a hierarchical control strategy, consisting of first-level and second-level controllers, is proposed by simultaneously considering the vehicle kinematics and dynamics. In particular, the vehicle kinematic model-based controller is proposed in first-level by incorporating the nonlinear coupling and interaction between the CVs and the heterogeneous communication delays; and the vehicle dynamic model-based adaptive integral sliding-mode controller is designed in second-level according to the vehicle desired states obtained by the first-level controller and external disturbance. In addition, the delay-dependent convergence of the first-level controller and finite-time tracking of the second-level controller are rigorously analyzed, respectively. Further, the infinity-norm method is used to investigate the string stability. Eventually, the performance of the developed hierarchical control strategy is verified by extensive simulations, co-simulations and compared with existing methods.