Over-The-Air (OTA) software updates are widely used in automotive embedded systems to remotely address software defects and vulnerabilities. However, the distribution of software packages is vulnerable to malicious attacks, posing severe security threats. Various cryptographic algorithms are used to secure automotive OTA software updates. However, existing secure OTA software updates rely on digital certificates for identity authentication. These digital certificates are often provided by third-party Certificate Authorities (CAs) and issued based on physical identifiers (e.g., Vehicle Identification Number (VIN), engine number, or Electronic Control Unit-ID (ECU-ID)), which are susceptible to illegal modification. Additionally, these secure OTA software updates fail to provide integrated security that encompasses authentication, confidentiality, integrity, access control, and data freshness. To tackle these existing drawbacks, we propose LigSecOTA, a lightweight OTA software update with integrated security based on a one-machine-one-certificate digital identity management system. The one-machine-one-certificate digital identity management system issues a unique and trusted digital certificate for each ECU based on bit time information instead of physical identifiers; these certificates are then used for ECU authentication. LigSecOTA ensures integrated security, including authentication, confidentiality, integrity, access control, and data freshness, through three processes: authentication, authorization, and package distribution. The authorization dy namically provides keys for the package distribution, significantly enhancing security. The security attributes of LigSecOTA are formally verified using the ProVerif tool. Finally, we evaluate LigSecOTA on the NXP LS1028A platform with an ARM Cortex A72 core. Experimental results demonstrate that LigSecOTA outperforms state-of-the-art secure OTA software updates in terms of computation and communication overhead, highlighting its lightweight nature.

In recent years, end-to-end autonomous driving has achieved remarkable progress, yet challenges remain in achieving efficient deployment. Existing methods often rely on multi-modal sensor fusion, which incurs high costs and computational demands, limiting their large-scale adoption in low-cost scenarios. Reducing system cost while maintaining performance has consistently been a key focus of the industry. Moreover, the opaque nature of end-to-end models, due to the lack of intermediate representations, limits interpretability and hampers root-cause analysis when misjudgments occur in complex environments, thereby posing safety risks. To address these challenges, this paper proposes a lightweight and interpretable end-to-end autonomous driving architecture that utilizes only monocular camera images as input. The aim is to enhance decision-making efficiency and trajectory planning accuracy while ensuring system safety. The proposed architecture comprises two core components. First, in the scene perception part, a depth information supervision module is introduced to enable hierarchical geometric perception, and an online vector map construction module is designed to build structured environmental representations at low cost. This replaces high-definition maps with explicit constraint expressions, thereby improving model interpretability and decision-making reliability. Second, in the planning part, a dual-layer prediction framework is developed to decouple speed and waypoint prediction. It independently forecasts vehicle velocity and waypoints, while an optimized decoder structure produces fine-grained spatiotemporal trajectories, enhancing planning capabilities in complex traffic scenarios. Experimental results on multiple benchmarks from the CARLA Leaderboard 1.0 demonstrate that the proposed method significantly improves system efficiency while maintaining advanced decision-making performance. It outperforms existing multi-sensor fusion approaches, showcasing strong potential for practical deployment.

Adopting Model-Based Development (MBD) in automotive software becomes necessary because it provides a rigorous development process. As the increase in the number of software components (SWCs) and their interactions, modifications to one part of the software might impact other parts due to the high coupling of SWCs. Developing an automated software refactoring technique to implement high cohesion and low coupling of software is necessary. Software refactoring usually contains code refactoring and architecture refactoring. Code refactoring only modifies the code inside the SWCs without changing its original functionality and external behavior, whereas current architecture refactoring does not consider the interaction weight (strength) between SWCs.In this study, we propose a weighted community division algorithm called Weighted-Girvan-Newman (W-GN) for software architecture refactoring. W-GN algorithm refactors the architecture by dividing SWCs into modules based on the interaction weights of SWCs. We further develop an automated architecture refactoring tool called AutoToolMD. This tool assists engineers in improving development efficiency and ensures the architecture aligns with the requirements of high cohesion and low coupling. Evaluations show that W-GN indicates more suitable modularity values than the traditional Girvan-Newman (GN) algorithm because the former can better reflect the real architecture with interaction weights. We apply AutoToolMD to automatically refactor architecture in automotive industry practice. Case study with the Zone_B PwrSplyMngt and Zone L_Lock modules in the Honda zone control software shows that AutoToolMD effectively refactors architecture with a significant improvement in testing efficiency and readability.

As promising industrial embedded networks, Controller Area Networks with Flexible Data-rate (CAN-FD) are widely used in time-sensitive domains, such as automotive networks. However, the absence of built-in security mechanisms in CAN-FD necessitates the development of security protection mechanisms. The existing Lightweight Authentication for Secure Automotive Networks (LASAN) framework focuses on enhancing the security of CAN/CAN-FD communication but neglects the conflict between security and delay. In this study, we conduct a thorough analysis of the causal mechanism related to the security and delay of LASAN and propose a static message scheduling method called Cooperative Swapping Approach (CSA) to achieve secure and low-delay CAN-FD communication. CSA is to minimize the end-to-end delay of precedence-constrained CAN-FD applications by swapping message positions in a valid message sequence. Nevertheless, exchanging message positions may impact the precedence dependencies between messages; therefore, we propose a novel Cooperative Transform Approach (CTA) within the CSA to efficiently preserve these precedence constraints. Valid message sequences with minimal end-to-end delays of a motivation example and an Adaptive Cruise Control (ACC) application are obtained in LASAN by CSA. These sequences are implemented on the embedded microcontroller platform of STM32H743IITs for evaluation. Experimental results show that our proposed CSA can effectively reduce the end-to-end delay of LASAN and outperform the state-of-the-art static message scheduling method in terms of low delay.

Controller Area Network with Flexible Data-rate (CAN-FD) is a promising industrial embedded network because of its high bandwidth and long data field length. However, CAN-FD does not deploy any security protection mechanisms, leaving it vulnerable to network attacks. In recent years, authentication and authorization frameworks have often been deployed in industrial embedded networks (e.g., automotive networks) to provide secure CAN/CAN-FD communication. However, these frameworks cannot simultaneously enhance confidentiality, integrity, and availability; moreover, these frameworks are mainly based on a distributed security management mechanism, resulting in large computation, communication, and memory overhead. This paper proposes a trinity-enabled and lightweight security framework called TrinitySec based on cryptographic algorithms for CAN-FD communication. TrinitySec ensures the availability of ECU and CAN-FD messages through authentication and authorization, as well as the confidentiality and integrity of CAN-FD messages through a symmetric-key algorithm and Hash-based Message Authentication Code (HMAC) function. TrinitySec proposes a low-overhead centralized security management mechanism instead of the existing distributed management mechanism. We formally verify the security of TrinitySec using the ProVerif tool. We implement TrinitySec on STM32H743IIT Micro Controller Unit (MCU) with ARM Cortex M7 core and evaluate that TrinitySec outperforms other state-of-the-art security frameworks in terms of computation, communication, memory, and storage overhead.

The rapid development of intelligent vehicles brings new challenges to vehicle EMC design, which is benefited from test data-oriented troubleshooting. With the increase in electronic complexity, vehicle on-board system designers should confront with more and more EMC failure possibilities and are in need of effective EMC failure diagnosis approach. However, EMC fault diagnosis is difficult due to the distinguishing features of EMC test dataset, such as small sample, nonlinear, high dimensions, etc. Hence, in this paper, EMC reverse diagnostic model is proposed. Firstly, EMC feature extraction method is designed. Then SVM model is designed to realize EMC faults classification. Corresponding application effect is displayed. Experiment results show that proposed method could match the demand of EMC fault diagnosis for intelligent vehicles.

Bad repeatability is a defect of on-road V2X (Ve-hicle to Everything) related test. The outside electromagnetic environment is un-controllable and the test outputs perform very un-stable. An obvious advantage of in-chamber test is the controllability of test environment parameters, which could mend the defect of on-road field test. In this paper, an in-chamber test scheme is designed for V2X related test. Corresponding operation conditions are analyzed, and test cases are defined in detail. Moreover, the test parameter setting method is given. A typical application, FCW, is test to verify the effectiveness of proposed scheme. Test results show that the output of in-chamber test is stable, which illustrate a good repeatability.

As the core technology of vehicle navigation system, path planning algorithm, which makes a great impact on the performance of navigation, has attracted substantial attention. However, the lack of predictive ability to traffic impedance leads to estimation error of road traffic. In addition, the geometry based link division method results in low computational efficiency. In this paper, a traffic impedance function, which considers historical accident factor, historical traffic factor, and real-time traffic factor, is proposed. Moreover, an accident hotspot oriented link division method is designed to reduce running time. Based on proposed impedance function, an improved A* algorithm is presented. The experimental results show that the proposed algorithm can improve path-searching efficiency, and show the ability to provide drivers with paths that are aware of real traffic condition and thus likely shorter travel time.

With rapid development of intelligent connected vehicles and VANET (vehicular ad-hoc network), electromagnetic environment that vehicles are facing becomes more complex than ever. The popular multi-function on-board radio terminals include various wireless blocks, such as mobile phone, FM radio, Wi-Fi hotspot, GPS receiver and Bluetooth transceiver, etc. All these blocks are working in a circumstance full of radio energy. Both academic circles and professionals concern if the upcoming intelligent connected vehicle and the radio terminals on-board could withstand the increasingly complex electromagnetic environment, and keep a good performance under high power radiation. Unfortunately, until today, there is no commonly agreed specification for such kind of test. In this paper, the connected vehicles, together with the on-board radio terminal, are discussed to find out a solution for their EMC test, while immunity pre-test platform is designed and corresponding test schemes are presented.

On-road risk assessment is one of the important topics for intelligent vehicles research. Efficiency of risk assessment is affected by lack of on-road vehicles information, which should be solved by The Vehicular Ad-Hoc Network (VANET). In this paper, a regional vehicles hazard level estimation method, which employs Ising model to explain the inter-vehicles relationship, is proposed, an inter-vehicles relationship model is constructed, and corresponding parameters mapping relationship is studied. Then Ising model's energy formula is used to illustrate regional hazard level. Moreover, based on the inter-vehicles relationship, we present an approach to build a regional threat distribution map, which indicates the passing threat of a certain ego-car. A series of experiments are done to examine the efficiency of the proposed method. Experiment results show that the proposed method can produce a reasonable danger evaluation for an ego-car.

Functional test is critical to the deployment of intelligent connected vehicle (ICV). Both industry and academia devote to create a testing framework, which could provide a reliable, quick, safe, low cost and reproducible method and accelerate the development of ICV. However, currently mainstream testing method, such as virtual test and on-road field test are sometimes difficult to satisfy the needs. In this paper, a semi-virtual test method, which employ semi anechoic chamber to construct operating environment of ICV, is proposed. Corresponding tests are done in semi-chamber of China Automotive Engineering Research Institute Co., Ltd (CAERI) to verify the effectiveness of proposed method. Test results show that proposed method is helpful for ICV function evaluation.