Achieving safe and efficient cooperative maneuvering in mixed highway traffic constitutes a significant engineering challenge, primarily due to the complex spatiotemporal coupling of multi-vehicle interactions and the inherent conflict between risk suppression and operational efficiency. This paper proposes a hierarchical cooperative control framework that formulates the overtaking task as a team-level constrained multi-agent Markov game with explicit safety budgets. Departing from heuristic penalty tuning or traditional safety layers, we introduce a Lagrangian-based policy optimization mechanism that models safety as a decoupled cost constraint and employs adaptive dual updates to regulate the safety–efficiency trade-off dynamically. A coordination-oriented reward and cost scheme is constructed to guide multiple agents in learning reciprocal yielding and efficient passing behaviors during complex interactions. Structurally, the framework integrates a hierarchical execution interface that maps high-level semantic maneuvers to low-level kinematic references. Under stochastic mixed traffic in highway-env, the proposed method limits the collision rate to 0.28% in the Extended traffic suite while maintaining improved TTC-based safety margins and robust time-gap performance. In addition, zero-shot transfer to the CARLA simulator achieves a 100% success rate with zero collisions under continuous vehicle dynamics and closed-loop control, demonstrating that the learned cooperative behavior remains executable beyond the original training environment. This work provides a scalable and reproducible solution for autonomous vehicle coordination under mixed traffic.

Decision-making and control are the core functionalities of high-level autonomous driving systems. Existing mainstream research, including modular and end-to-end paradigms, typically employ conservative strategies that compromise driving efficiency. However, driving efficiency constitutes a critical constraint on the transition of autonomous vehicles from mere operability to practical utility. Autonomous overtaking systems serve as a typical means to improve driving efficiency. Nevertheless, in stochastic and uncertain traffic scenarios, achieving safe and efficient continuous autonomous overtaking remains a significant challenge. In this context, this paper proposes a decision-making and control framework based on MeUAL to achieve the optimal trade-off between overtaking risk and efficiency. First, at the decision-making layer, a safe reinforcement learning method based on Uncertainty-aware Augmented Lagrangian (UAL) is developed to provide global overtaking guidance. Subsequently, the motion planning and control layer based on Model Predictive Control (MPC) closely tracks the UAL-generated guidance, while preserving the safety and constraint guarantees inherent to traditional MPC. Finally, a Policy Switching Mechanism (PSM) triggered by the safety epistemic uncertainty threshold is designed for the MeUAL-driven autonomous overtaking system. Experimental results demonstrate that MeUAL outperforms baseline algorithms with respect to reward-cost balance, sample efficiency, and learning stability. Moreover, in various test scenarios that are distinct from the training distribution, MeUAL-PSM exhibits strong robustness and interpretable overtaking maneuvers through flexible policy switching.

Global climate change has emerged as a pressing global challenge, underscoring the imperative for governments and urban traffic management authorities to prioritize carbon emission reduction in the transportation sector. In mixed traffic environments, where internal combustion engine vehicles and electric vehicles coexist, the disparity in carbon emissions between these vehicle types poses a significant challenge for the formulation of effective transportation coordination policies. Consensus-based speed advisory systems (CSAS) have been extensively employed to enhance fleet energy efficiency and mitigate emissions. This paper develops a novel vehicle speed advisory framework for mixed traffic environments, termed the MP-CSAS, where M stands for Mixed Traffic and P for Privacy-preserving, which leverages blockchain technology, privacy-preserving mechanisms, and secure car-following strategies. By incorporating a coordination factor, the framework enables policymakers to dynamically optimize carbon emission reduction strategies while safeguarding vehicle user data privacy and ensuring operational safety. Simulation results demonstrate that the MP-CSAS framework effectively minimizes fleet carbon emissions while preserving data confidentiality and ensuring system security. This study contributes a forward-looking decision-making paradigm for road infrastructure providers and policymakers, equipping them with scientifically grounded and adaptive strategies to achieve sustainable and safe transportation objectives.

Accurate vehicle trajectory prediction process depends on seamless data sharing within the Internet of Vehicles. However, such interconnected data exchange introduces significant security risks. Specifically, network attacks can compromise data integrity, thereby degrading prediction accuracy. Concurrently, the need to protect sensitive vehicle data, such as driving trajectories and user account information, results in data silos that hinder the free flow of information essential for effective prediction. Existing studies have largely addressed either privacy preservation or attack mitigation in isolation, lacking a unified solution that simultaneously tackles both challenges. To address this gap, we propose Fed-SecTP, an integrated dual-module secure federated learning framework. The first module employs a Temporal Convolutional Network (TCN) with multi-head attention to detect and filter network attacks in real-time. The second module combines TCN with a Bidirectional Long Short-Term Memory (Bi-LSTM) network for trajectory prediction and leverages FedProx for federated learning, thereby enabling privacy-preserving model training without sharing raw data. Experimental results demonstrate that Fed-SecTP achieves high prediction accuracy and robustness even when up to 50% of the data is compromised by attacks, while ensuring secure data processing. This framework offers a reliable and comprehensive solution for autonomous vehicle trajectory prediction.

Modular distributed electric drive heavy-duty vehicles (MDED-HDV) integrate advanced technologies such as all-wheel steering (AWS) and distributed drive, achieving complete decoupling of the chassis’ motion degrees of freedom (DoFs). This architecture is considered a promising solution for enhancing the stability of heavy-duty vehicles (HDV). However, the impact mechanism of multi-axle steering configurations on stability remains inadequately understood, and the redundancy in control DoFs results in multiple feasible steering configurations. To address these challenges, this paper proposes a hierarchical control framework featuring steering mode switching based on stability region constraints. First, a dynamics model of MDED-HDV is established using rational polynomials. Subsequently, the sum-of-squares programming (SOSP) is employed to estimate the stability region, providing the first analysis of the effects of multi-axle steering on the stability region from the perspective of nonlinear system dynamics. Based on this analysis, a stability region-based steering mode switching strategy is developed. It incorporates vehicle states and road conditions to enable autonomous transitions among anti-phase, front-wheel, and in-phase steering modes. Finally, a hierarchical control framework is implemented. The upper layer selects the steering mode based on the estimated stability region. The lower layer executes a trajectory tracking controller with stability region constraints. The framework addresses the issue of multiple solutions caused by redundant DoFs. Experimental results demonstrate that the proposed steering mode switching strategy improves the tracking accuracy, while the stability region-based controller ensures maneuver stability.

Human-like decision-making and planning are crucial for advancing the decision-making level of autonomous driving and increasing acceptance in the autonomous vehicle market, as well as for achieving data closed loop for autonomous driving. However, human-like decision-making and planning methods still face challenges in safety and interpretability, particularly in multi-vehicle interaction scenarios. In light of this, this paper proposes an interpretable human-like decision-making and planning method with Transformer-based deep inverse reinforcement learning. The proposed method employs a Transformer encoder to extract features from the scenario and determine the attention assigned by the ego vehicle to each traffic vehicle, thereby improving the interpretability of planning outcomes. Furthermore, for improved safety in planning, the model is trained on both positive and negative expert demonstrations. The experimental results show that the proposed method enhances model safety while maintaining imitation levels compared to conventional methods. Additionally, the attention allocation results closely align with those of human drivers, indicating the model’s ability to elucidate the importance of each traffic vehicle for decision-making and planning, thereby improving interpretability. Therefore, the proposed method not only ensures high levels of imitation and safety but also enhances interpretability by providing accurate attention allocation results for decision-making and planning. Note to Practitioners—This paper presents a method for enhancing the planning of autonomous vehicles by making it more interpretable and safer. Using Transformer-based deep reinforcement learning, the approach improves clarity by showing how the vehicle prioritizes other traffic participants and learning from both positive and negative examples. This not only enhances safety and decision accuracy but also provides insights into the vehicle’s reasoning process, which is crucial for debugging and increasing user trust. Future work could focus on adapting this method for even more complex driving scenarios.

Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this study proposes an overtaking-enabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the host vehicle’s energy consumption and travel delay. The OEAC adopts a two-stage receding horizon control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle’s speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin’s minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively.

This paper presents an adaptive leading cruise control strategy for the automated vehicle (AV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where AVs and HDVs share the road, the longitudinal speed control of AVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both AV and HDV are incorporated into the reward function of reinforcement learning (RL). The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of AV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. In freeway driving scenarios, simulation results indicate that the holistic energy efficiency is improved by an average of 6.03% and 6.41% compared to the reference control algorithms, specifically, RL without HDV consideration and model predictive control. These improvements highlight the significance of our approach in optimizing energy efficiency for mixed traffic on freeways.

Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can effectively improve traffic throughput and vehicle fuel efficiency at highway on-ramp merging bottlenecks. However, in the mixed traffic scenario where CAVs and human-driven vehicles (HDVs) coexist, the uncertain maneuvers of human drivers pose a major challenge to merging control in terms of safety and flexibility. To this end, this paper proposes a hierarchical cooperative on-ramp merging control strategy for CAVs to optimize flexible trajectories with safety guarantees in mixed traffic. First, the on-ramp merging control problem for CAVs is considered in the case of a three-vehicle coordination, resulting in an optimal control problem (OCP) coordinating on-ramp and main-lane CAVs for efficient operation while satisfying multiple safety-critical constraints. Second, a two-level hierarchical control architecture is developed to solve the OCP with mixed state-control constraints. The upper-level planner solves an unconstrained OCP with Pontryagin’s Minimum Principle to calculate an expected merging position, which is embedded in the variable time headway of safe merging constraints in the lower-level controller. Then, the controller converts the nonlinear OCP with safety-critical constraints to a quadratic programing (QP) problem by exploiting Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). By solving the QP efficiently, the time and energy efficient trajectory for each CAV is obtained. In addition, a receding horizon control framework is employed, which enables CAVs to determine flexible merging opportunity and tackle the disturbances caused by HDVs. Finally, comprehensive simulation results show that the proposed cooperative on-ramp merging strategy has potential in enabling merging flexibility, improving traffic efficiency and energy economy in real time.

Aggressive driving behavior has greatly endangered vehicle safety and posed challenges to the design of advanced driver-assistance systems (ADASs). A novel driver–automation cooperative shared control system is proposed in this article to make steering assistance actions better cooperate with aggressive drivers. Based on investigating shared control modes, a driving activity parameter for drivers is introduced, which aims to modulate the shared control authority and mitigate the conflicts between aggressive drivers and ADAS. A polytope represented by finite vertices is employed to handle uncertain parameters, including driving activity and longitudinal velocity. Then, an H$\infty $robust output-feedback control method satisfying the regional pole assignment is presented to provide robustness and stability of the polytope space while simplifying the control structure through reducing vertices. The driver-in-the-loop simulator experiments are carried out to verify the proposed controller, in which the driver model parameters are identified. The results demonstrate that the developed assistance controller can effectively ensure path-tracking accuracy and simultaneously improve aggressive drivers’ comfort.

The eco-driving strategy that targets driving speed optimization is recognized as a promising technique to improve vehicle energy efficiency. However, it is difficult to achieve real-time eco-driving control of hybrid electric vehicle (HEV) since the speed optimization and powertrain energy management should be resolved simultaneously. This paper proposes a hierarchical control architecture consisting of learning-based velocity planner and real-time energy management system. In the upper stage, Proximal Policy optimization (PPO) agent is trained to generate acceleration which meets multiple control objectives. The lower stage adopts Equivalent Consumption Minimization Strategy (ECMS) for real-time power split control considering powertrain dynamics. Finally, the eco-driving simulations of six signalized intersections in Nanjing are conducted. Compared with two different rule-based strategies, the proposed control architecture can achieve at least 7.39% of fuel economy saving and avoid a significant drop in the battery state of charge at the expense of higher than 5% of travel time. Simulation results also prove that the proposed strategy has an energy-saving potential in unseen scenarios.

There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as “pseudo-measurements”, into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.

Vehicle active suspension systems provide possibility to bring better ride comfort, handling stability and driving safety with proper control than passive suspension. This paper utilizes deep reinforcement learning method to develop active suspension systems due to its good generalization. The controller is based on a quarter-car active suspension model, and suspension dynamic characteristics are analyzed under the condition of bump disturbance. Simulation results show that the performance of active suspension tends to be stable after proper training. Compared with the passive suspension and the Skyhook-based suspension, the deep reinforcement learning-based active suspension can reduce the vehicle body acceleration more effectively and further improve the ride comfort without sacrificing the suspension deflection and dynamic tire load. Deep reinforcement learning-based active suspension can still maintain good performance after switching bump heights or vehicle speed which verifies good generalization of the controller.

Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system.