Scan chain design is the most widely used design-for-testability (DFT) technique, enhancing the controllability and observability of internal circuit states to improve test efficiency while reducing complexity and cost. However, this capability also introduces security risks, as attackers can exploit scan chains to extract sensitive information such as encryption keys. While scan-based attacks have been well-established for various ciphers such as AES, research on SM4-the commercial block cipher standard of China-remains largely underexplored. To address this gap, this paper presents the first comprehensive scan-based side-channel attack on SM4. Our method introduces four key innovations: (i) an efficient register localization technique requiring only two plaintexts, (ii) a universal Hamming distance lookup table customized for SM4’s structure to recover byte keys, (iii) adaptive plaintext construction for attacking different encryption rounds, and (iv) a dedicated parity-check mechanism for byte-key verification. Experimental results successfully demonstrate the extraction of four consecutive round keys, validating the practical feasibility of the attack. This work highlights a significant security threat to SM4 implementations and underscores the need for secure DFT practices.

Interacting with diverse and stochastic traffic participants is a critical challenge for autonomous vehicles (AVs), as it necessitates advanced decision-making systems to replicate the natural adaptability of human drivers. In particular, navigating safely and efficiently in dense traffic scenarios poses a significant challenge for decision-making, which is inherently an interactive task,i.e., nearby traffic participants will influence AVs’ action, and vice versa. Decision-making solutions that rely solely on unidirectional interaction schemes, neglecting the mutual influence between AVs and other traffic participants, may lead to overly defensive behaviors or the “freezing robot problem”. In recent years, researchers have been increasingly focused on incorporating bidirectional interactions into the decision-making process to make safe, intelligent, and socially compatible decisions. Currently, a comprehensive review of interaction-aware decision-making techniques remains lacking. To this end, this paper aims to provide a systematic review of interaction-aware decision-making methodologies for autonomous driving. Specifically, this paper analyzes the challenges in considering bidirectional interactions between AVs and other traffic participants. In addition, the state-of-the-art techniques for interaction-aware decision-making solutions are reviewed. More importantly, simulation and benchmarks for interaction-aware decision-making validation are also presented. Finally, research perspectives are highlighted to facilitate future studies for interaction-aware decision-making policy design.

4D millimeter-wave radar, as a fundamental sensor for 3D object detection, has gained increasing attention in autonomous driving due to its robustness and additional elevation information. However, the sparsity and noisiness of 4D radar point clouds hinder its broader application. Fusing camera with 4D millimeter-wave radar provides an affordable and robust solution. In this paper, a fast and effective framework HP-C4D for camera and 4D radar fusion is proposed. Three key modules are proposed in HP-C4D. Firstly, we innovatively propose the Height BEVPool to predict the height of each BEV grid with negligible delay increase during the height compression. The predicted height information is incorporated into the final object height prediction. Secondly, Perceptive View Heatmap Guidance (PVHG) is proposed to suppress background noise's generation in image BEV and assist the Height BEVPool for better height prediction. Thirdly, Interactive Guidance Fusion (IGF) is proposed to efficiently fuse image BEV and 4D radar BEV. Extensive experiments on the View-of-Delft (VoD) and TJ4DRadset datasets demonstrate the effectiveness and advance of our proposed method. It is worth highlighting that the 3D mean average precision of our proposed method is 0.71% and 3.85% higher than the latest baseline method LXL (LiDAR-Excluded-Lean) on the VoD and TJ4DRadset datasets, respectively. To the best of our knowledge, HP-C4D is the fastest method for camera and 4D radar fusion in 3D object detection on the VoD dataset, achieving 21.4 FPS on single NVIDIA RTX 3090 GPU. Code will be released at https://github.com/c-yyyy/HP-C4D.

Reinforcement learning (RL) offers a powerful paradigm for adaptive motion planning in complex driving environments. However, applying RL to autonomous driving remains challenging due to uncertainty from partial observability and the stochastic, multimodal behaviors of traffic participants. This paper presents a novel risk-aware RL framework for non-conservative motion planning under uncertainty. By integrating Partially Observable Markov Decision Processes (POMDP) with a deep RL-based policy optimization scheme, the proposed approach explicitly models aleatoric uncertainty via a Gaussian Mixture Bayesian Belief Updater and a time-varying risk field. Additionally, an Adaptive Context-aware Attention (ACA) module is employed to prioritize critical targets for enhanced interaction modeling dynamically. Extensive experiments on the CARLA simulator show that the framework generalizes well across diverse traffic conditions, improving average reward by 65.74% and 64.02% in low-speed dense and high-speed sparse scenarios. It remains robust in challenging situations such as overtaking and sudden lane changes in the PeMS dataset. Furthermore, distributed deployment tests confirm a real-time performance of 10 Hz on a hardware-in-the-loop platform, demonstrating the feasibility of practical deployment.

Driving on public roads is inherently an interactive task, i.e., autonomous vehicles’ (AVs) actions will influence nearby traffic participants’ reactions, and vice versa. Decision-making for AVs in highly interactive driving scenarios (e.g., dense traffic) requires accurately forecasting the impact of the AVs’ intention on nearby traffic participants’ motion. To this end, a hierarchical decision-making framework (HDM) is proposed to navigate through interactive scenarios safely and efficiently. Specifically, the upper layer of the HDM serves as a coarse-level policy generator, which utilizes the plan-informed graph attention network (P-GAT) to provide interaction-aware guidance. The P-GAT predictor takes the historical states of nearby traffic participants, road structure information, and AVs’ potential intentions as inputs. Subsequently, it predicts the motions of other traffic participants in response to potential actions of the AVs, which is then systematically evaluated to generate interactive guidance. Furthermore, the learned policy is utilized to guide the lower layer, which utilizes a fine-level model predictive control (MPC)-based planner to ensure safety and kinematic feasibility. Finally, to validate the effectiveness of HDM, both qualitative and quantitative experiments are carried out. More importantly, the hardware-in-the-loop (HiL) experiment is also implemented, including mandatory lane change in dense traffic flow and interaction with the human driver. The results demonstrate that the proposed HDM can improve driving safety and efficiency compared with baselines.

The active crash avoidance system of intelligent vehicles faces challenges in accurately triggering and achieving effective multiobjective coordination control in complex driving environments characterized by multiple vehicles and variable road conditions, thereby posing a threat to driving safety for human. To address those issues, this article introduces a decision-making, path planning, and tracking integrated predictive control method (DMIC). First, DMIC incorporates vehicle actuation system characteristics, road conditions, and environmental information to design a dynamic characteristic-based risk indicator, which is applied to design different control modes. DMIC then applies continuous activation functions to activate optimized states and control inputs under various control modes, forming an integrated event-triggered continuous decision-making objective function. Afterward, DMIC employs the receding horizon optimization to calculate the front-wheel steering angle and wheel torques based on the integrated predictive model and time-varying constraints. Verification on a driver-in-the-loop (DiL) platform demonstrates that DMIC can accurately and smoothly switch among different optimization states, ensuring crash avoidance with arbitrary approaching vehicles under varying road conditions, thereby maintaining smooth vehicle responses, enhancing the driving stability and ride comfort, while meeting real-time and robustness requirements.

Fuel cell vehicles (FCVs) are considered a promising solution for reducing emissions caused by the transportation sector. An energy management strategy (EMS) is undeniably essential in increasing hydrogen economy, component lifetime, and driving range. While the existing EMSs provide a range of performance levels, they suffer from significant shortcomings in robustness, durability, and adaptability, which prohibit the FCV from reaching its full potential in the vehicle industry. After introducing the fundamental EMS problem, this review article provides a detailed description of the FCV powertrain system modeling, including typical modeling, degradation modeling, and thermal modeling, for designing an EMS. Subsequently, an in-depth analysis of various EMS evolutions, including rule-based and optimization-based, is carried out, along with a thorough review of the recent advances. Unlike similar studies, this paper mainly highlights the significance of the latest contributions, such as advanced control theories, optimization algorithms, artificial intelligence (AI), and multi-stack fuel cell systems (MFCSs). Afterward, the verification methods of EMSs are classified and summarized. Ultimately, this work illuminates future research directions and prospects from multi-disciplinary standpoints for the first time. The overarching goal of this work is to stimulate more innovative thoughts and solutions for improving the operational performance, efficiency, and safety of FCV powertrains.

As one of the alternatives to conventional fuel vehicles, hybrid electric vehicles (HEV) offer lower fuel consumption and fewer exhaust emissions. To improve the performance of the HEV, the energy management strategy (EMS) is one of the most critical technologies. Classic EMS can be broadly classified into rule-based and optimization-based. With the development of machine learning technology, the deep reinforcement learning (DRL) algorithm of intelligent learning algorithms has been applied to the EMS. This paper mainly reviews the research progress of the EMS based on DRL from two aspects of the algorithm and training environment, and the EMS research involving combining the intelligent transportation system (ITS) is reviewed. In addition, the experimental test progress situations of DRL-based EMS research are discussed. Finally, the challenge of DRL-based EMSs is analyzed and some solutions are provided. In particular, it also involves some discussion about automotive cyber security in the intelligent transportation environment.

Reinforcement learning (RL) has been widely used in the decision-making of autonomous vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal policy by maximizing the expectation of future returns, which lacks distributional treatments of risky situations. Additionally, various uncertainties arising from the environment could also cause unreliable decisions, particularly in some complex urban environments. In this paper, the fully parameterized quantile network (FPQN) is utilized to estimate the full return distribution. Then, the conditional value-at-risk (CVaR) is utilized with the return distribution information to generate uncertainty-aware driving behavior. Additionally, an uncontrolled four-way intersection is developed by the Simulation of Urban Mobility (SUMO) simulation platform, which considers both the surrounding vehicles (SVs) and pedestrians. More specifically, to simulate the real-world traffic environment, the uncertainty arising from the occlusion, and the behavior uncertainty of surrounding traffic participants are also considered. The experiment results suggest that the proposed method outperforms the baseline methods in terms of safety. Furthermore, the results also indicate that the proposed method can make reasonable decisions in some challenging driving cases in the presence of uncertainty.

Reliable and accurate velocity prediction can significantly contribute to the quality of connected vehicle control applications. Existing efforts focus on the velocity prediction without considering vehicle-to-vehicle (V2V) communication interruption. Hence, a stochastic velocity prediction method for connected vehicles considering V2V communication interruption is put forward for the first time. The missing V2V communication data are addressed by the piecewise cubic Hermite spline interpolation. Then, the processed data are used as the input variables of the best conditional linear Gaussian (CLG) prediction model. Specifically, the best CLG model is obtained by analyzing the influence of different input variables on the velocity prediction without V2V communication interruption. The results demonstrate that the prediction accuracy of CLG-based model is acceptable if the communication interruption time is less than 5 s compared to the non-interrupted V2V communication case. The sensitivity study of the best CLG model under multiple vehicles scenario indicates that choosing appropriate historical data substantially improve the prediction accuracy. Furthermore, the CLG-based predictor is proved to be an effective method to achieve higher prediction accuracy in two test road networks when compared with the Back-propagation and Long Short-Term Memory network.

Motion prediction modules are crucial for autonomous vehicles to forecast the future behavior of surrounding road users. Failures in prediction modules can mislead a downstream planner to make unsafe decisions. Currently, deep learning technology has been widely used to design prediction models due to its impressive performance. However, such models may fail in long-tail driving scenarios where the training data are insufficient or unavailable, which represents the so-called epistemic uncertainty of prediction models. This paper proposes a risk-aware decision-making (RADM) framework to handle the epistemic uncertainty arising from training the prediction model on insufficient data. First, a multi-agent prediction network with epistemic uncertainty quantification is proposed. This network uses the historical states of nearby road users, map information, and traffic lights as inputs. Then, the RADM utilizes model predictive control technique to not only process the multi-agent prediction results but also to consider the epistemic uncertainty of the prediction model. In addition, the accuracy of the established prediction model is verified on real-world driving datasets. Furthermore, the proposed RADM is evaluated on the log-replay data obtained from real-world driving logs and using the SUMO simulator, considering multiple challenging cases where pedestrians and non-motorized vehicles cross the intersection illegally. The experimental results demonstrate that RADM can reduce the driving risk and improve driving safety and supplementary videos are provided athttps://github.com/SOTIF-AVLab/RADM.

Autonomous driving is considered one of the revolutionary technologies shaping humanity’s future mobility and quality of life. However, safety remains a critical hurdle in the way of commercialization and widespread deployment of autonomous vehicles on public roads. Safety concerns require the autonomous driving system to handle uncertainties from multiple sources that are either preexisting, e.g., the stochastic behavior of traffic participants or scenario occlusion, or introduced as a result of processing, e.g., the application of neural networks. Thus, it is crucial to analyze the sources of uncertainties and quantify the risks associated with them, including the propagated risks that accumulate in the decision-making system. In this context, this paper provides an overview of uncertainty challenges and state-of-the-art techniques for mitigating these challenges. We argue that the uncertainties mainly originate from two aspects: 1) the external traffic environment, and 2) the internal autonomous driving system. Specifically, this paper first analyzes the safety challenges caused by the uncertainties and summarizes their sources. In addition, the corresponding techniques that mitigate and quantify the risk of uncertainties are presented. Finally, research perspectives are highlighted to facilitate future studies for guaranteeing the safety of autonomous vehicles.

The increasing requirements for vehicle driving safety improvement have led to numerous and in-depth studies on vehicle stability, especially for autonomous vehicles. The main concerns of vehicle stability research in autonomous vehicles include the vehicle stability analyzing, criterion constructing and controller designing. Therefore, this paper provides a comprehensive review of state-of-the-art vehicle stability criterion and control application for autonomous vehicles. First, the lateral vehicle linear stability criterion and widely-used active stability control applications are introduced. Next, the nonlinear vehicle stability analysis algorithm and criterion, based on the well-known phase plane method, are discussed in detail. The stability controller design, including the activation strategy and tracking objectives, is reviewed. In addition, emerging research challenges and trends for future improvement in lateral stabilization of autonomous vehicles are finally summarized.

With the improvements in sensor performance (cameras, Lidars) and the application of deep learning in object detection, autonomous vehicles (AVs) are gradually becoming more notable. After 2019, AV has produced a wave of enthusiasm, and many papers on object detection were published, boasting both practicality and innovation. Due to hardware limitations, it is difficult to accomplish accurate and reliable environment perception using a single sensor. However, multi-sensor fusion technology provides an acceptable solution. Considering the AV cost and object detection accuracy, both the traditional and existing literature on object detection using image and point-cloud was reviewed in this paper. Additionally, for the fusion-based structure, the object detection method was categorized in this paper based on the image and point-cloud fusion types: early fusion, deep fusion, and late fusion. Moreover, a clear explanation of these categories was provided including both the advantages and limitations. Finally, the opportunities and challenges the environment perception may face in the future were assessed.

As one of adaptive optimal controls, the Q-learning based supervisory control for hybrid electric vehicle (HEV) energy management is rarely studied for its adaptability. In real-world driving scenarios, conditions such as vehicle loads, road conditions and traffic conditions may vary. If these changes occur and the vehicle supervisory control does not adapt to it, the resulting fuel economy may not be optimal. To our best knowledge, for the first time, the study investigates the adaptability of Q-learning based supervisory control for HEVs. A comprehensive analysis is presented for the adaptability interpretation with three varying factors: driving cycle, vehicle load condition, and road grade. A parallel HEV architecture is considered and Q-learning is used as the reinforcement learning algorithm to control the torque split between the engine and the electric motor. Model Predictive Control, Equivalent consumption minimization strategy and thermostatic control strategy are implemented for comparison. The Q-learning based supervisory control shows strong adaptability under different conditions, and it leads the fuel economy among four supervisory controls in all three varying conditions.

Vesicular stomatitis virus (VSV) is the prototype for negative sense non segmented (NNS) RNA viruses which include potent human and animal pathogens such as Rabies, Ebola and measles. The polymerases of NNS RNA viruses only initiate transcription at or near the 3' end of their genome template. We measured the dissociation constant of VSV polymerases from their whole genome template to be 20 pM. Given this low dissociation constant, initiation and sustainability of transcription becomes nontrivial. To explore possible mechanisms, we simulated the first hour of transcription using Monte Carlo methods and show that a one-time initial dissociation of all polymerases during entry is not sufficient to sustain transcription. We further show that efficient transcription requires a sliding mechanism for non-transcribing polymerases and can be realized with different polymerase-polymerase interactions and distinct template topologies. In conclusion, we highlight a model in which collisions between transcribing and sliding non-transcribing polymerases result in release of the non-transcribing polymerases allowing for redistribution of polymerases between separate templates during transcription and suggest specific experiments to further test these mechanisms.