In human-machine (autonomous vehicles and robots) interaction scenarios, pedestrians often appear in groups. Pedestrian groups provide richer information compared to individuals, which helps address occlusion problems in pedestrian-machine interactions. However, the randomness and spatiotemporal complexity of pedestrian activity make pedestrian group activity recognition (PGAR) a highly challenging task. This article provides a detailed description of the PGAR task. For the first time, a definition of pedestrian group and activity for autonomous vehicles and robots is provided. Existing datasets and methods are systematically summarized. Furthermore, the unique challenges and trends in PGAR for autonomous vehicles and robots are outlined. Although some related surveys have been published, there has not yet been a survey specifically focused on PGAR in autonomous driving and robotics scenarios. Therefore, the goal of this article is to narrow the gap in this topic and provide a comprehensive reference for researchers in this field.

The connected hybrid vehicles (CHVs) fleet, which consists of multiple inter-CHVs, serves as a significant driver of future intelligent transportation systems. Through vehicle-to-vehicle (V2V) communication, CHVs can greatly enhance driving safety and reduce fuel consumption. However, network exposure and frequent communication make CHVs highly vulnerable to information security attacks, particularly Denial of Service (DoS) attacks, which could potentially lead to communication interruptions and pose a threat to the safety of the fleet. Therefore, designing an advanced vehicular control strategy such that the control performance of CHVs is resilient to DoS attacks has become an urgent issue. To address this challenge, this article proposes an attack-resilient control strategy to ensure the security and cost-effectiveness of the CHVs in the event of a DoS attack. First, heterogeneous and uncertain vehicle dynamics models and DoS attack models are established. Building upon this foundation, a comprehensive resilient model predictive control (CRMPC) strategy is proposed, which ensures queue safety through defined safety functions and resilient MPC while enhancing economy by incorporating vehicle fuel consumption function. Then, a weight adjustment mechanism is employed to balance the relationship between security and cost-effectiveness. Furthermore, an adaptive equivalent consumption minimization strategy (A-ECMS) is adopted, with the equivalent factor being dynamically adjusted in real-time using a Proportional Integral algorithm. Finally, the results of two test scenarios demonstrate that the strategy improves fuel efficiency by 5.32% and 5.44%, respectively, while ensuring fleet safety.

With the development of automotive electrification, more application of electrical devices increases the demand for high-power electrical consumption, especially electric drive technology. Heavy-duty vehicles would recommend more powerful, dynamic and rapid powertrains. Powertrains are the dynamic heart of the vehicles, and series hybrid electric powertrains (SHEP) proposed in this paper are particularly suitable for above demand due to their high energy density and load-bearing capabilities. On the contrary, the electromechanical coupling, nonlinear dynamics, and complex control architectures of the powertrains increase the challenges to stable operating states. To elucidate the mechanisms underlying the instable states in SHEP, this paper introduces which state is instability and the issue at first. Second, the energy models are established, which provide the method to analyze the energy flow in SHEP. Third, the instable state is analyzed by the law of conservation of energy and experimental data. It can be concluded that the imbalance between energy supply and consumption dynamically leads to the instable state in SHEP.

In heavy-duty series hybrid electric vehicles (SHEVs), engine-generator set (EGS) functions as the main power source for propulsion. However, limitation of engine power per liter and delayed computation of control algorithm result in the hysteretic response of EGS to high demand power. It leads to deteriorating operation of powertrain. Thus, challenging technical issue lies in achieving stable powertrain operation which is difficult to describe precisely by real-time control. In this work, an efficient power control scheme for heavy-duty HEV with online optimized variable universe fuzzy system is proposed. First, a splitting sequential clustering quadratic programming (SSCQP) algorithm is designed to solve power distribution and achieve real-time control. The original subproblem is split into two subproblems with smaller scale to obtain iterative points. And clustering algorithm is introduced to gather up the points to improve the termination criterion. It turns to skip unnecessary short step in the iteration which fails to obtain sufficient descent. Then, the online optimized variable universe fuzzy system is established to achieve rapid response of EGS by adjusting power distribution. In this system, online optimization of membership function distribution parameters is considered. The optimization is constructed on real-time membership overlap degree and central value of fuzzy system rather than the traditional off-line optimization using posterior information of vehicle. Finally, effectiveness of proposed scheme is validated both in simulation test and hardware-in-loop test. The results reveal that stable power output is maintained and calculation time is decreased by 40.9%, 46.0% under two driving cycles.

Vehicle trajectory prediction plays a crucial role in ensuring the driving safety of autonomous vehicles in complex traffic scenes. To accurately predict the trajectory of autonomous vehicles, in this article, we propose a distributed decouple long short-term memory (LSTM) self-trajectory prediction method for autonomous driving. The proposed new recurrent network includes a decouple-LSTM unit and corresponding distributed network architecture. To characterize the closed-loop dynamics of autonomous vehicles, a decouple gate and a control gate are proposed to build the decouple-LSTM unit. The data are processed in different ways according to whether the data participates in the recurrent. The decouple gate filters the data participating in the recurrent, while the control gate handles the data outside the recurrent. By leveraging the decouple-LSTM unit, a distributed network architecture is established, which corresponds with the general vehicle motion control architecture, which effectively models the vehicle motion processes. The proposed method is trained using an actual vehicle dataset and validated through vehicle experiments. The prediction horizon ranges from 0.5 to 3 s. When the prediction horizon is set to 3 s, compared with the LSTM method, the mean square error of the proposed method decreases by 98.0%. Results show that the proposed method significantly improves vehicle trajectory prediction accuracy.

Automotive electrification is a major trend for environmentally friendly transportation. Hybrid electric vehicles are also gaining popularity as a key transitional technology. Coordination control is crucial in improving the operation efficiency for series hybrid electric powertrains (S-HEPs), which involves determining the output power of multiple units such as the engine-generator set (EGS), battery, and motor. However, due to nonlinearity and the electromechanical dynamic difference between each unit, the powertrain state, such as engine speed and direct current link voltage, is prone to fluctuation. So, a high-performance coordinated control strategy is urgently needed. To address this problem, this article proposes a self-triggered model predictive control (MPC) method with adaptive prediction horizon for S-HEPs. Unlike traditional unit-independent feedback control schemes, this article proposes a system-integrated control scheme by establishing a multi-input and multioutput control model that integrates the EGS, battery, and motor. The model is then translated into a linear optimization control problem with input constraints applied into MPC. To reduce the computing burdens of MPC, a mechanism of self-triggered with adaptive prediction horizon is designed by considering the state dispersion and future state deviation. Finally, a hardware-in-the-loop experiment and a simulation experiment are conducted to validate the efficiency of the proposed self-triggered MPC. The results show that the proposed control method achieves a more desirable powertrain state compared to the conventional stability-voltage strategy, and the proposed self-triggered MPC reduces about 60% computing burdens while maintaining similar tracking performance to normal MPC.

This study proposes a sequential clustering quadratic programming (SCQP) method for the energy management strategies of plug-in hybrid electric vehicles (PHEVs). In this method, the clustering algorithm is introduced to gather up the points with a smaller iteration step size in the iteration process. The clustering results are utilized to design the termination criterion based on the distance between the cluster centers of various iteration domains. In the case that the distance varies within the preset range, it indicates that the current iteration point is sufficiently close to the optimal point. So that the criterion turns to terminate the computation to reduce unnecessary iteration steps. To analyze the convergence of the method with the designed criterion, the mathematical illustrations are proposed. In the mathematical illustrations, the monotonicity of the clustering objective function is firstly given. Then, the theorem of feasibility for the solution obtained by the designed criterion is proved. On the basis of aforementioned conclusions, the convergence of the SCQP method is obtained. Finally, the performance of the proposed method is validated both in simulation test and hardware-in-loop (HIL) test. The simulation results reveal that the PHEV achieves 8.81% and 7.74% less fuel consumption under two driving cycles. And the average iteration number of the proposed method is obviously reduced compared with the conventional SQP. The HIL results reveal that the proposed strategy exhibits similar performance in both real controller and simulation. The energy saving and real-time performance can be verified.

Vehicle chassis coordinated control always has been an appealing topic in academia and industry because of the increasing number of chassis electronic actuators with the rapid development of automated vehicles. The optimization of multiple performance targets with multiactuators is intractable, which involves trajectory tracking and handling stability. Additionally, the optimization of tire friction usage remains a knotty problem. Therefore, this article develops a global chassis multiactuator integrated control framework, named by the chassis domain controller (CDC), to realize chassis global dynamics optimization for automated vehicles. Aiming at realizing more efficient, reliable, and flexible mobility, this framework defines each individual wheel to be fully adjustable and controllable to overcome the individual actuation limitation of traditional chassis structures. Global chassis dynamic modeling is formulated based on the analysis of distributed and controllable tire modules and vehicle dynamics motions. A game-theoretical control scheme is proposed to formulate chassis multiactuator integrated control, and the chassis global dynamics can be optimized by guaranteeing a Nash equilibrium for this game. Various experimental results demonstrate the feasibility and effectiveness of the proposed control method, and it suggests the CDC merits further studies to enhance the dynamics performance of automated vehicles in full situations.

For automated driving vehicles, trajectory planning is responsible for obtaining feasible trajectories with velocity profiles according to driving environments. From the perspective of trajectory planning, multiple uncertainties of environments and tracking deviations are two significant factors affecting driving safety. The former disturbs the judgment of trajectory planning on the environments, and the latter reduces the tracking accuracy of planned trajectories. To solve these problems, an improved model predictive control (MPC) trajectory planning method is proposed in this paper. Firstly, a Kalman filter fusion method is carried out to predict obstacle trajectory and their uncertainty, which combines model-based and data-based prediction methods. Based on the prediction results, a tube-based MPC trajectory planning method is applied to plan a reference trajectory with a small tracking deviation. The tube-based MPC is composed of two parts. One is the MPC with tightened constraints that is used to plan a feasible trajectory according to a nominal vehicle system and driving environment. The other is a state feedback control that is proposed to adjust the above planned trajectory to reduce the tracking deviations. To our knowledge, this paper proposes Kalman filter fusion and tube-based MPC planning method for the first time to consider the uncertainties of trajectory prediction and tracking control meanwhile in the planning. The planning method is verified by simulations and experiments in multiple scenes. Results show that the method is suitable for both static and dynamic scenes. Compared with applying the basic prediction method, the lateral deviation of the proposed method from the ideal trajectory is decreased by 46.5%. Compared with the nominal MPC method, the lateral tracking deviations of the proposed method are decreased by 77.42%.

Due to inevitable parameter uncertainties and disturbances, four-wheel independent drive automated vehicles (4WIDAVs) will produce tracking deviation during the path following process, which have a negative impact on driving safety. Meanwhile, the over-actuated feature of 4WIDAVs will also increase the deviation if not properly handled. To solve this problem, a specific adaptive model predictive control strategy for path following of 4WIDAVs is proposed. Firstly, to obtain a real-time and accurate vehicle dynamics model, the recursive least square method is used to estimate the time-varying uncertainty of tire cornering stiffness. Secondly, based on the real-time updating system model, the modified tube-based model predictive control method is applied to realize path following under the influence of the disturbance. Meanwhile, the compensating yaw moment for controlling vehicle is generated by the designed torque distribution algorithm, which makes full use of the over-actuated feature of 4WIDAVs. Finally, different maneuvers are performed both in simulation and experiment. Results show that the proposed strategy can achieve more accurate path following than the traditional model predictive control and linear quadratic regulator. Compared with the existing controller, the path following accuracy is improved by 41.6% and 60% in simulation and experiment, respectively. Therefore, the proposed strategy is proved to be effective, which provides a theoretical reference for vehicle control in reality.

Plug-in hybrid electric buses (PHEBs), compared with traditional fuel-driven vehicles, can achieve higher fuel economy and lower pollution emissions. For a PHEB with a single-shaft parallel powertrain, a major challenge for researchers is to find approximate optimal energy management strategies that can run in real time. Motivated by this idea, this paper aims at minimizing PHEB fuel consumption with a temporal-difference (TD) learning method. First, historical driving cycle data from real-world bus routes are collected and processed and parameter variables of TD are introduced. Specially, this process is completed offline. Then, the configuration and main parameters of PHEB are presented, and a control-oriented dynamic system of the PHEB is constructed. Thereafter, the TD learning method based on historical data is introduced. Furthermore, the approximate optimal control strategy for energy management is proposed. Compared with the traditional optimal control strategy, the proposed method can realize real-time running without sacrificing the accuracy of optimization, because the learning method updates the estimates based on other learned estimates without calculating a final outcome. This method can learn directly from the data of running PHEBs without a simplified model of the PHEB, which can avoid the influence of model error. Finally, to verify this method, several different strategies are used for comparison. In addition, experimental results in real-world driving cycles demonstrate that the proposed method can improve the fuel economy obviously by up to 21% compared with a traditional charge-deleting, charge-sustaining scenario. Therefore, this novel method has great potential in realistic applications.

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver's demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver's driving style and promote fuel economy.

Driving cycles and road slope are two important factors affecting fuel saving performance of plug-in hybrid electric buses (PHEBs) in Chinese cities. Moreover, onboard auxiliary equipment (e.g., Global Position System receiver and General Packet Radio Service (GPRS) wireless module) of PHEB may provide potential means to communicate with the control center of the bus company, allowing for driving cycle prediction through data communication between foregoing buses and the control center. With this general approach in mind, and by utilizing driving data clustering and driving cycle classifier, this paper presents a multimode switched logic control strategy, targeting fuel economy improvement of the PHEB team for a particular city bus route. First, the normal feature parameters are extracted from the sampled driving history cycles, and the composed feature parameters are given by a mapping of normal feature parameters in this approach. A novel improved hierarchical clustering algorithm is applied for driving cycles' data clustering into four groups. Then, on the basis of the clustering results, support vector machine method is used to predict the current driving cycle. Finally, a switched driving controller is presented according to current type of driving cycle and slope information. Simulation results are compared with those of traditional methods in the given real-world driving cycles of city bus, showing significant improvement, which may offer a theoretical solution with engineering application. Experimental results also demonstrate that the proposed control approach is feasible in the tested bus routes.