Safe trajectory planning in uncertain environments is critical for autonomous driving. However, keeping the safety of vehicles under uncertainty is an open and challenging problem. The key challenges are how to predict and quantify the trajectory uncertainty of other traffic participants, and perform high-quality real-time trajectory planning in dynamic and complex environments. To address these challenges, this paper presents an uncertainty-aware safe trajectory planner based on model predictive control, which considers the uncertain trajectory of the target vehicle and enhances the system safety. To predict the trajectory uncertainty of the target vehicle, a trajectory prediction method combining kinematics and reachable set is proposed, which can reduce the conservatism compared to robust invariant set. To ensure vehicle safety in uncertain environments, an uncertainty-aware safe trajectory planner is established, which extend the control barrier function to uncertain system, and the control barrier function safety constraints are constructed based on the probabilistic n-step reachable set of the target vehicle trajectory. In addition, safety constraints including safe distance from the target vehicle, vehicle handling stability, road boundary, actuator saturation constraints are taken into account. Finally, simulation results show that the proposed planner can improve the system safety and feasibility in uncertain environments compared with other baseline methods. Additionally, its robustness is validated by analyzing the impact of different levels of uncertainty in complex scenarios. Moreover, the real-time performance is verified by the hardware-in-the-loop experiment, which proves that the planner can be applied in real-world autonomous vehicle systems.

Compared with traditional pipelined frameworks, end-to-end autonomous driving integrates the problems of perception, decision-making, planning, and control, which can be more adaptive to complex scenarios and more generalizable. However, in end-to-end automated driving, especially when in complex urban roads with high-dimensional observation data, reinforcement learning training is difficult and inefficient, and intelligence exploration is difficult to find the best driving strategy. To address the above problems, in this paper, we propose to use maximized value conditional state entropy(VCSE) reinforcement learning for automatic driving strategy learning. The method estimates the conditional value of each state, also known as the conditional state entropy, and then maximizes their average value, increasing the state entropy to encourage the intelligent body to explore the environment, and at the same time, limiting the state entropy to prevent the excessive pursuit of state entropy from causing the intelligent body to prefer exploring the low-value state. In this paper, the value-conditional state entropy is used as the intrinsic reward, which is weighted and summed with the task reward, so as to encourage the intelligent body to explore the environment and improve training efficiency. We use the exact same environment and traffic flow for training and evaluation in the Carla simulator, and our method shows a large performance improvement in all aspects and better exploration utilization of the environment compared to the baseline method.

This paper proposed a timescale graph-parallel (GP) computation method to solve the real-time optimization problem of nonlinear predictive energy-saving control, thus to realize the implementation of MPC on vehicle on-board controllers. The proposed scheme consists of two parts: forward prediction of the objective function and backpropagation of the partial differential function, both of which can be calculated in parallel. Thus, compared with traditional serial solution method for optimization problems, the timescale graph-parallel computation method can utilize the computing resources of the controller fully. In this paper, firstly, based on the characteristics of commercial vehicles, a mixed integral optimal control problem (MIOCP) was constructed. Then, a detailed timescale graph-parallel computation algorithm was derived for the MIOCP. Finally, GP and Pontryagin’s Minimum Principle (PMP) algorithms were applied on the predefined road for the simulation of the prediction of energy-saving control for commercial vehicles. The simulation results showed that compared with PMP, the maximum iteration number, average iteration number, single longest solution time, and single average solution time of the proposed GP decreased by 60%, 64.28%, 89.53%, and 93.56%, respectively. In addition, GP can also improve fuel efficiency by 1.55% without sacrificing much power performance.

Connectivity technologies in intelligent transportation systems offer unprecedented opportunities to enhance mobility, fuel economy, and safety for automotive systems. However, the uncertain driving behavior of surrounding vehicles in real-world traffic scenarios can significantly undermine these benefits. To tackle this challenge, this article develops a stochastic predictive-adaptive cruise control (P-ACC) system that effectively addresses uncertainties and automatically adapts to various driving scenarios. The proposed system employs a Gaussian process (GP)-based velocity predictor as its foundation, accurately capturing the driving dynamics of the preceding vehicle while accounting for prediction uncertainty using variances. The real-time feasibility is assessed in a dSPACE rapid prototyping system. In addition, the developed stochastic-model predictive control (S-MPC) approach incorporates the predicted velocity variance into the probabilistic chance constraints, conservatively narrowing the optimization space of the velocity planning domain, thereby enabling more reliable control. To further enhance the system’s performance in adapting to different driving conditions, a scenario-based parameter self-learning (PSL) technique is introduced in the S-MPC controller, utilizing Bayesian optimization (BO). Finally, the performance of the proposed controller is comprehensively evaluated by leveraging a high-fidelity simulator and on-board actual vehicle testing data. Simulation results demonstrate that the proposed method achieved a boost in tracking performance and driving comfort while maintaining fuel-saving benefits.

The rapid development of connected vehicles (CVs) has offered novel opportunities for eco-driving control. Considering inherent spatial and temporal constraints from the preceding vehicle, multiple signalized intersections, and queues, this paper proposes a hierarchical energy-efficient control strategy (HCS) in different domains to reduce fuel consumption and travel time. Considering both traffic lights and queue information, the concept of virtual traffic lights is proposed based on queue estimation. In the higher-level controller, a distance-based energy-economy velocity optimization problem is formulated to treat spatial constraints from virtual traffic lights and queues. The optimal velocity profile is solved by the direct multiple shooting algorithm in a model predictive control (MPC) framework, which is used as a reference by the lower-level controller. To treat temporal constraints of safe inter-vehicular time, a predictive cruise control (PCC) in the time domain is introduced in the lower-level controller to ensure safe inter-vehicle distances and improve fuel efficiency while tracking the reference speed. Comparative simulation results show that the proposed strategy can significantly reduce fuel consumption and travel time.

In this article, a systematic assessment of cyber-physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.

This study aims to develop an optimal cruise controller that can automatically adapt to individual car-following style. First, the adaptive cruise control (ACC) problem is formulated as a linear quadratic optimal control, and an optimal control law containing the longitudinal acceleration of the target vehicle is derived. Then, a certain number of individual car-following styles are predefined on the basis of the proposed optimal cruise controller. Thereafter, a car-following style learning algorithm is proposed to quantify the closeness of the predefined individual car-following style to the specific driver, and a proper style is thus determined for the specific driver by using this learning algorithm. On the basis of the learned car-following style, the proposed optimal cruise controller can adapt itself to individual car-following style. Finally, the proposed self-learning optimal cruise controller is evaluated through simulation and experimental tests. Results show that the control behavior of the proposed self-learning optimal controller is closer to that of the human driver than that of a factory-installed ACC.

The roundabout is a typical changeable, interactive scenario in which automated vehicles should make adaptive and safe decisions. In this article, an optimization embedded reinforcement learning (OERL) is proposed to achieve adaptive decision-making under the roundabout. The promotion is the modified actor of the Actor-Critic framework, which embeds the model-based optimization method in reinforcement learning to explore continuous behaviors in action space directly. Therefore, the proposed method can determine the macroscale behavior (change lane or not) and medium-scale behaviors of desired acceleration and action time simultaneously with high sample efficiency. When scenarios change, medium-scale behaviors can be adjusted timely by the embedded direct search method, promoting the adaptability of decision-making. More notably, the modified actor matches human drivers' behaviors, macroscale behavior captures the human mind's jump, and medium-scale behaviors are preferentially adjusted through driving skills. To enable the agent adapts to different types of the roundabout, task representation is designed to restructure the policy network. In experiments, the algorithm efficiency and the learned driving strategy are compared with decision-making containing macroscale behavior and constant medium-scale behaviors of the desired acceleration and action time. To investigate the adaptability, the performance under an untrained type of roundabout and two more dangerous situations are simulated to verify that the proposed method changes the decisions with changeable scenarios accordingly. The results show that the proposed method has high algorithm efficiency and better system performance.

Internet of Things (IoT) enables extensive connections between cyber and physical "things". Nevertheless, the streaming data among IoT sensors bring "big data" issues, for example, large data volumes, data redundancy, lack of scalability and so on. Under "big data" circumstances, IoT system monitoring becomes a challenge. Furthermore, cyberattacks which threaten IoT security are hard to be detected. In this paper, we propose an online distributed IoT security monitoring algorithm (ODIS). An advanced influential point selection operation extracts important information from multidimensional time series data across distributed sensor nodes based on the spatial and temporal data dependence structure. Then, an accurate data structure model is constructed to capture the IoT system behaviors. Next, hypothesis testing is carried out to quantify the uncertainty of the monitoring tasks. Besides, the distributed system architecture solves the scalability issue. Using a real sensor network testbed, we commit cyberattacks to an IoT system with different patterns and strengths. The proposed ODIS algorithm demonstrates promising detection and monitoring performances.

In this article, a systematic and generalized methodology is originally proposed to assess the vulnerability of electric drive systems due to sensor data integrity attacks. Novel evaluation metrics from the perspectives of steady-state and transient performance of electric drive systems are established to evaluate the system condition under different attacks. By using these metrics, innovative index-based resilience and security criteria, together with the stability theorem, are proposed specifically for electric drive systems, which can then be used for cyber-attack detection and diagnosis in a more systematic manner. Then, based on the simulation results under 15 attack cases (five typical types), the qualitative attack impacts on the dynamic characteristics and the statistical damage of different cyber-attacks to the defined metrics are analyzed, which can serve as useful guidelines for attack detection, diagnosis, and countermeasures.

This paper proposes a predictive cruise control based on eco-driving for a passage car that uses the information of upcoming traffic limits and the preceding vehicle to realize better fuel economy. To fully exploit the inherent potential of the powertrain system to reduce fuel consumption, the velocity is obtained by optimizing the engine torque, the brake force, and the gearshift while ensuring safe distance separation and traffic speed limits. The problem is described as a nonlinear mixed-integer problem and solved by the concept of combining Pontryagin’s minimum principle and bisection method. The simulation results show a significant improvement in computational efficiency compared with traditional numerical methods, and the simulation results also show that the computational time increases linearly with prediction horizon. It is shown that an improvement of 8% in fuel is achieved in a realistic scenario compared with a basic vehicle using a standard adaptive cruise control.

Velocity profile optimization of on-road vehicles is one of the main eco-driving techniques, which has great potential to extend the capability of powertrain and automatic longitudinal control by minimizing the energy consumption. Due to the multi factors affecting the driving trajectory and longer prediction horizon comparing with other traditional control, the calculation of a velocity profile optimization often requires a large number of computations. In this paper, a hierarchical control (HC) strategy of velocity optimization is proposed to reduce computation burden with little accuracy loss. In the HC strategy, a specific driving task is divided into several operation of modes as acceleration (A), constant speed (C), deceleration (D), and braking (B). The shift timing of the driving modes are optimized by formulating a nonlinear programming problem in a master controller. Then, engine torque, gear position, and brake force are optimized in each driving mode. Results indicate that the computation time of velocity profile optimization using the proposed HC strategy is reduced by 90% of the ones using the basic centralized optimal controller while the resulting velocities are similar. It is also shown that an improvement of 30% in fuel economy is achieved compared with the real-life human-driven velocity profiles.

Wide usage of vehicle's onboard navigation system offers vehicles better terms to improve energy efficiency. In this paper, a computationally effective energy management strategy using model predictive control (MPC) is proposed to find the energy optimal torque split, gear shift, and velocity control of a parallel hybrid electric vehicle (HEV). We consider the vehicles in urban driving, where the vehicle trajectory is constrained by the infrastructure (road signs) and other vehicles (traffic). Restricted by the discrete gear ratio, nonlinear dynamics of the vehicles, and especially different time scales between velocity trajectory and torque split optimization, finding these control variables in one optimal problem is quite challenging. Thus, this paper uses bi-level methodology to reduce computational time and simplify the hybrid optimal problem by decoupling its components into two subproblems. In the outer loop, the optimal velocity trajectory is obtained by solving a nonlinear time-varying optimal problem using a Krylov subspace method to improve computational efficiency. In the second subproblem, we provide an explicit solution of the optimal torque split ratio and gear shift schedule by combining Pontryagin's minimum principle and numerical methods in the framework of MPC. Simulation results on an AMESim model of an HEV with seven-speed automated manual transmission over multiple driving cycles are presented. The results indicate that both energy efficiency and computational speed are improved.