Large vision-language models (LVLMs) have demonstrated remarkable capabilities in autonomous driving scene understanding tasks. However, these models occasionally generate hallucinatory texts, resulting in descriptions that seem reasonable but do not correspond to the image. To address this challenge, this paper proposes HELTA, a training-free data annotation method used in traffic scenarios, which is designed to support the offline generation of high-quality semantic datasets without hallucinations. Specifically, HELTA employs a cross-checking mechanism to filter entities and directly extracts critical objects from the given image, enriching the descriptive text. Experimental results on the POPE benchmark demonstrate that HELTA improves the F1-score of the Mini-InternVL-4B and mPLUG-Owl3 models by 12.58% and 4.28%, respectively. Additionally, qualitative results using images collected in open campus scene further highlight the practical applicability of the proposed method. Compared with the GPT-4o model, HELTA achieves comparable descriptive performance while significantly reducing costs. Finally, two high-quality semantic understanding datasets, CODA_desc and nuScenes_desc, are created for traffic scenarios to support future research. The codes and datasets are publicly available athttps://github.com/fjq-tongji/HELTA

In order to improve the environmental adaptation and safety of autonomous vehicles trajectory planning process in a complex driving environment, a novel trajectory planning method which meets the requirement of multidriving tasks and adapts to various driving conditions is proposed in this article. In the trajectory planning method, the optimal control problem considering multiple driving tasks is established based on the constructed performance function and constraint analysis of different driving tasks to ensure the accurate realization of driving tasks. Besides, the neural network empirical model, precollision detection model, and trajectory evaluation model are designed by the consideration of selecting the optimal planning parameters in different driving conditions to enhance the adaptability to traffic environment. The advantage of the proposed method is that it not only meets the requirements of a variety of driving tasks, but also able to select the optimal planning parameters according to different traffic conditions while existing methods usually only meet single planning task, such as lane change, and has the fixed and rigid parameter selection. Four different typical scenarios are given to verify the effectiveness of the proposed method and the results show that the proposed trajectory planning method is able to ensure the safety of the vehicle and adapt to different traffic environments flexibly.

Trajectory optimization in multi-vehicle scenarios faces challenges due to its non-linear, non-convex properties and sensitivity to initial values, making interactions between vehicles difficult to control. In this paper, inspired by topological planning, we propose a differentiable local homotopy invariant metric to model the interactions. By incorporating this topological metric as a constraint into multi-vehicle trajectory optimization, our framework is capable of generating multiple interactive trajectories from the same initial values, achieving controllable interactions as well as supporting user-designed interaction patterns. Extensive experiments demonstrate its superior optimality and efficiency over existing methods. We will release open-source code to advance relative research1.

Reinforcement learning (RL) has become a key method for decision-making in autonomous vehicles, particularly in tasks like path planning and obstacle avoidance. However, RL often struggles with sparse or delayed reward signals, which can hinder learning. A promising solution is incorporating a teacher-student framework, where the agent learns from a teacher. While these methods can accelerate learning, they risk sub-optimal outcomes if the teacher's guidance is flawed. This paper introduces Double Entropy Reinforcement Learning (DERL), which enables the student to initially rely on the teacher and gradually shift towards independent exploration as it surpasses the teacher's performance. Experiments show that DERL outperforms existing teacher-student algorithms in learning efficiency and effectiveness, reducing reliance on imperfect guidance.

In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordingly. The unpredictability of human behaviors poses significant challenges, particularly in dynamic scenarios, such as multi-lane highways and unsignalized T-intersections. To address this gap, we design a bi-level interaction decision-making algorithm (BIDA) that integrates interactive Monte Carlo tree search (MCTS) with deep reinforcement learning (DRL), aiming to enhance interaction rationality, efficiency and safety of AVs in dynamic key traffic scenarios. Specifically, we adopt three types of DRL algorithms to construct a reliable value network and policy network, which guide the online deduction process of interactive MCTS by assisting in value update and node selection. Then, a dynamic trajectory planner and a trajectory tracking controller are designed and implemented in CARLA to ensure smooth execution of planned maneuvers. Experimental evaluations demonstrate that our BIDA not only enhances interactive deduction and reduces computational costs, but also outperforms other latest benchmarks, which exhibits superior safety, efficiency and interaction rationality under varying traffic conditions.

The generation of unknown unsafe scenarios is crucial for the verification and validation of automated vehicles. However, the complexity of traffic and the sensitivity of testing costs pose significant challenges in reducing the unknown unsafe region. To address this problem, a framework is proposed for generating unknown unsafe scenarios based on hierarchical classification. Firstly, the critical scenario library is obtained using an evolving simulation system. Secondly, the scenarios in the scenario library are hierarchically classified based on the maneuver, position, and motion of the vehicles, resulting in abstract scenarios. Finally, a concretization method is employed to search for potential unsafe scenarios within these abstract scenarios. This methodology was applied to the testing of the Stackelberg algorithm. The evolving system generated 6142 scenarios. These were then classified using the abstraction method, yielding 35 abstract scenarios. For the abstract scenario with the highest hazard rate of 19.47%, the concretization method was used to design logical scenarios. Based on an optimization algorithm, 443 potential concrete unsafe scenarios were identified.

In order to prevent the overheating of motors and enhance vehicle stability, a centralized fault prevention control method based on model predictive control that incorporates a motor thermal model is proposed. The motor thermal model is separated into heat generation and heat dissipation components, precisely capturing the impact of motor torque on temperature variations. By dynamically adjusting motor torque distribution, it effectively regulates motor temperature, extends motors lifespan, and addresses the research gap in the coupling of motor thermal management and vehicle motion control. Besides, it coordinates the control of motor torques, steering angles, and active suspension forces within a unified centralized architecture, thereby avoiding response delays and performance conflicts that may arise from distributed control framework, achieving a balance between vehicle dynamics and thermal protection. Finally, the effectiveness of the proposed method in preventing motor overheating and controlling vehicle stability while maintaining ride comfort is validated through hardware-in-the-loop testing.

This paper proposes an online co-optimization strategy for velocity planning and powertrain control in autonomous vehicles (AVs), aiming to improve energy efficiency and travel time in urban driving. A hierarchical eco-driving framework is designed to incorporate both long-horizon traffic signal information and short-horizon traffic dynamics. In the upper layer, velocity planning is formulated as a mixed-integer optimal control problem (MIOCP) in the distance domain. Binary variables are introduced using the big-M method to model disjunctive constraints associated with signalized intersection crossing decisions. The lower layer ensures velocity eco-tracking and car-following safety in the time domain while co-optimizing gearshift, traction, and braking torque, also resulting in an MIOCP. To address the real-time computation challenge, a two-stage optimization method is introduced. First, a neural network (NN) is trained offline via supervised learning to predict feasible integer strategies based on MIOCP parameters. Then, the predicted integers are fixed online, and the resulting problem is solved as a nonlinear programming (NLP) problem. The effectiveness of the proposed method is thoroughly validated through extensive simulation studies under various urban scenarios, including multiple signalized intersections, varying road speed limits, and diverse preceding vehicle (PV) behaviors. The results demonstrate that the proposed approach enables safe and adaptive eco-driving, obtaining a 5.97% average improvement in energy efficiency over the intelligent driver model, while accelerating computation speed by 1–2 orders of magnitude compared to Bonmin and achieving millisecond-level computational time.

Due to the input of high-dimensional data in end-to-end autonomous driving, if the feature extraction network is trained online from scratch, it will lead to difficulties in learning and decision-making. To solve this challenging problem, this paper proposes a predefined multi-modal image method to improve decision-making accuracy and a shared framework to quickly train all networks. The inputs are composed of various observations including bird’s-eye view, state, and front view, rather than traditional sensor raw data. They can be filtered out ineffectual features based on human prior knowledge to avoid over-exploration with invalid changes, such as light, trees, mountains, etc. Combined with the processed representations, predefined images are formed by embedding the trajectory, forbidden line, etc. The method can assist agents to capture the coupling relationship between the actions and states, allowing networks to positively adjust weights to quickly obtain expected rewards. Considering that embedding a feature extraction network into reinforcement learning networks will lead to repeated cumulative calculations, a shared framework is proposed to independently compress features while jointly participates in online training to reduce computing costs. This module dynamically combines the policy and critic to update its weights, which overcomes decision-making problems caused by inaccurate the latent feature sequence during pre-training and fine-tuning methods. Finally, the interactive environment is constructed on a realistic driving simulator CARLA, and the fusion of different modal states is explored. The results showed that the multi-modal fusion can explore to earn maximum rewards, and the proposed methods are effective for the training.

The integration of look-ahead information into Model Predictive Control (MPC) frameworks has shown promise for intelligent transportation systems. However, transitioning Predictive Cruise Control (PCC) system research into practical application poses challenges due to numerous weighting parameters and increased computational demands in complex driving environments. Although the Weighted Sum Method is commonly used in PCC system research to balance fuel consumption and trip time objectives, it requires time-consuming weight tuning and often results in suboptimal performance due to fixed weighting parameters. To address this, this paper proposes a Utopia-tracking Model Predictive Control (UTM-MPC) controller, where the cost function is reformulated as the sum of the distances between the objectives and the average Utopia point over the prediction horizon. By analyzing the Pareto front of the PCC optimization problem under varying slope profiles extracted from digital map data, we demonstrate that the proposed UTM-MPC effectively leverages the geometric characteristics of the Pareto front to identify preferred trade-off solutions. The adaptive weighting mechanism—derived from the online-calculated Utopia point—enhances the robustness of the PCC system under complex and dynamic driving conditions. To mitigate the computational burden associated with integrating UTM-MPC into the MPC framework, we introduce a tailored neighboring extremal-based solving algorithm. Leveraging the receding horizon nature of MPC, this method requires only minimal updates to efficiently identify an optimal solution near the nominal trajectory from the previous sampling instance. Simulation results show that the UTM-MPC controller, with its adaptive weighting strategy, consistently outperforms the traditional Weighted Sum Method in terms of both fuel efficiency and trip time.

3D multi-object tracking is an important component of the perception module in autonomous driving systems. Due to the limitations of a single sensor, tracking methods based on either LiDAR or cameras always have certain deficiencies. Fusion-based tracking methods have received increasing attention. However, existing fusion-based tracking methods often underutilize image information, ignore the respective effects of appearance information and 2D detection results, and lack further analysis on the simultaneous use of both. This paper proposes a novel camera-LiDAR fusion tracking framework that primarily relies on the motion model using 3D objects. It fully leverages the appearance information and 2D detection results simultaneously from images and introduces three modules to reduce the number of false positive samples, false negative samples and ID switches, respectively. Besides, the entire tracking process does not require global processing and achieves online tracking. The proposed method achieves competitive results on the KITTI tracking dataset with 78.50% HOTA. Compared with EagerMOT using the same 3D and 2D detectors, the HOTA metric improved by 4.11%. Code is available on https://github.com/zengwz/FusionTrack.

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.

Due to the limitations of network communication conditions for online calling GPT, the onboard deployment of Large Language Models for autonomous driving is in need. In this paper, we propose Drive as Veteran, a fine-tuned LLaMA-7B model with driving tasks. A training set consisting of instructions, scenario descriptions and human-annotated driving tasks is established. Through LoRA fine-tuning, the capability of generating correct driving tasks of our model is demonstrated through a numerical experiment and the comparison to GPT-3.5 is presented. We show that smaller-sized Large Language Models could be deployed onboard with fast generation speed and high accuracy, which could serve as a core component for decision-making in autonomous driving.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Real-time and high-performance semantic segmentation is a crucial task in the scene understanding of autonomous vehicles. This paper focuses on this issue and proposes a transformer and convolutional neural networks (CNN) hybrid encoder-decoder structure SegTransConv. Firstly, we present a four-stage hierarchical encoder, and the feature extractor in each stage is composed of two transformer layers and CNN modules in series. In this way, the encoder better exploits the global contexts of the input and expands the receptive fields. In the U-shape decoder, the feature maps are upsampled through the proposed feature enhancement upsampling module (FE_Up). Then the knowledge distillation strategy is leveraged to improve the model performance under the guidance of the teacher network STDCNet. Finally, a novel evaluation metric is designed to comprehensively assess the accuracy, speed, floating-point operations (FLOPs), and parameters of real-time segmentation methods. Extensive experiments on two public datasets and self-collected images have evaluated the effectiveness of our method. SegTransConv-A and SegTransConv-B obtain 72.8% and 73.0% mIoU, respectively, at the inference speed of 68.0 FPS with an input resolution of$1024\times 512$.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

Cooperative perception techniques incorporating Vehicle-to-Everything (V2X) information offer new possibilities for enhancing the perception capability of automated vehicles (AVs), but also present a new challenge of how to maximize the benefits of connected information with limited communication burden. In this context, this paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information. Given the common multi-sensor configurations of AVs, we design a consensus-based distributed cooperative perception (DCP) algorithm in the framework of multi-layer information fusion for local sensors and connected nodes, and give a nonlinear form based on cubature rules to include more accurate nonlinear system models and nonlinear sensors. Considering the high maneuverability of vehicle targets, we then extend the DCP algorithm to a multi-model form (DMMCP) to improve the model uncertainty of maneuvering targets via combining the prior knowledge of multiple models, which also gives a calculation method for model probability and its average consensus in the context of multiple local sensors. Besides, a new consensus information weight strategy and the properties from different consensus information weights are discussed. The simulation results demonstrate the superiority of both our algorithms over the traditional algorithms in accuracy and consistency, moreover, the DMMCP algorithm, which takes into account model uncertainty, shows better performance than DCP in complex conditions.

Interactive decision-making between multiple automated vehicles under unsigned intersections is a high-level dynamic decision-making scenario, greatly increasing the complexity of decision-making. In this situation, making the decision-making manner in accordance with the logic of human and guaranteeing driving safety is technically challenging. A multi-factor-enabled interactive decision-making method is proposed in this paper to realize such behavior, which employs multiple complementary factors and switchable modes in a dynamic game. More specifically, these factors are driving performance requirements, e.g., moving safety, smoothness comfort, fast passing, and surrounding space, as well as diversified driving styles suitable for different driver groups. Meanwhile, to improve the reasonability of automated driving and reduce the complexity of multi-vehicle games, switchable game modes are established to realize the dynamic adjustment mechanism. The effectiveness of the proposed method in resolving conflicts in a continuous interactive way is verified through extensive simulations. The results indicate the proposed method can reflect the interaction process between multi-agents, and improve compliance between intelligent decision-making and the logic of human.

In order to resolve the driving conflict and improve the safety and efficiency of autonomous vehicles at unsignalized intersections, a new decision-making method based on game theory with personalized driving preferences considered is proposed in this paper. In the decision-making method, the alterable game mode is constructed by combing the designed game entrying mechanism and replanning of game sequential order under different intersection conditions to ensure the adaptiveness and effectiveness of the decision-making algorithm. Besides, four payoff indicators and personalized payoff function are designed with the consideration of driving efficiency, safety and comfort requirement. The advantage of proposed method is that it not only reduces the complexity of game mode and improves the effectiveness at the same time, but also fully considers the personalized driving preferences to realize human-like driving and personalized decision while the existing methods usually neglect the difference among the drivers and its influence. Five different typical scenarios at the unsignalized intersection are given under co-simulation environment of Matlab and Prescan. The results show that the proposed decision-making method is able to resolve the driving conflict and ensure the safe passage of autonomous vehicles at unsignalized intersections, which verifies the effectiveness of the proposed method.

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 order to improve the efficiency and safety of autonomous vehicles’ decision-making process in complex urban scenarios, a decision-making method for hierarchical processing of static traffic law information and dynamic traffic participant information is proposed in this paper. In the decision-making method, the candidate behavior set is constructed by extracting the element of traffic laws and fully considering the traffic laws constraints to ensure the legality and effectiveness of the decision-making algorithm. Besides, four evaluation indicators and two-level entry threshold are designed to select the optimal driving behavior with the consideration of driving efficiency, ride safety and macro path requirement. The advantage of proposed method is that it avoids the problem of poor adaptability to traffic laws and regulations as the existing methods usually make decisions under the condition of mixed traffic laws and traffic participant information. A complete driving task simulation and analysis, including six typical urban traffic scenarios, is given under Matlab environment. The results show that the proposed decision-making method is able to make reasonable and feasible decisions and highly consistent with the actual driver’s decision-making behavior in complex urban scenarios, which verifies the effectiveness of the proposed method.

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.

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.

Great advances in simulation-based vehicle system design and development of various driver assistance systems have enhanced the research on improved modeling of driver steering skills. However, little effort has been made on developing driver steering skill models while capturing the uncertainties or statistical properties of the vehicle-road system. In this paper, a stochastic model predictive control (SMPC) approach is proposed to model the driver steering skill, which effectively incorporates the random variations in the road friction and roughness, a multipoint preview approach, and a piecewise affine (PWA) model structure that are developed to mimic the driver's perception of the desired path and the nonlinear internal vehicle dynamics. The SMPC method is then used to generate a steering command by minimization of a cost function, including the lateral path error and ease of driver control. In the analyses, first, the experimental data of Hongqi HQ430 are used to validate the driver steering skill controller. Then, the parametric studies of control performance during a nonlinear steering maneuver are provided. Finally, further discussions about the driver's adaption and the indication on vehicle dynamics tuning are given. The proposed switching-based SMPC driver steering control framework offers a new approach for driver behavior modeling.

In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.