<div>Speed bump detection through computer vision and deep learning is essential for advancing active suspension preview control and intelligent driving. Although substantial progress has been made in this field, there remains a need to enhance detection accuracy while reducing computational demands. This article introduces a novel single-stage speed bump detector, the Speed Bump Detector Based on You Only Look Once (SBD-YOLO), which utilizes the YOLOv9 architecture for speed bump identification. To better capture the deep global features of speed bumps, we propose an innovative convolutional module—specifically, a lightweight building block designed for efficient feature extraction—named the Aggregated-MBConv. Furthermore, we design a new YOLO backbone by stacking Mobile Inverted Bottleneck Convolution (MBConv) and Aggregated-MBConv modules, which reduces computational cost while enhancing detection accuracy. Additionally, we introduce a Squeeze-aggregated Excitation (SaE) attention mechanism at the network’s neck, which, through parallel operation, enables collective integration across branches, further improving network performance. A dedicated speed bump dataset was created to validate SBD-YOLO’s effectiveness. Compared to YOLOv9, SBD-YOLO achieves a 9.3% increase in precision, a 2.5% boost in recall, and improvements of 2.2% and 1.4% in mean Average Precision at an Intersection-over-Union (IoU) threshold of 50% (mAP50) and mean Average Precision over IoU thresholds from 50% to 95% (mAP50-95), respectively. Moreover, the number of parameters is reduced by 5 million, and computational complexity is decreased by approximately 82.8%. These results demonstrate the significant potential of SBD-YOLO for active suspension preview control.</div>

<div>This article provides an overview of how the determination of absence of unreasonable risk can be operationalized. It complements previous theoretical work published by existing developers of automated driving systems (ADS) on the overall engineering practices and methodologies for readiness determination. Readiness determination is, at its core, a risk assessment process. It is aimed at evaluating the residual risk associated with a new ADS deployment. The article proposes methodological criteria to ground the readiness review process for an ADS release. Specifically, it lists 12 readiness criteria connected with system safety, cybersecurity, verification and validation, collision avoidance testing, predicted collision risks, impeded progress, rules of the road compliance, vulnerable road users interactions, high-severity assessment, conservative estimate of severity, risk management, and field safety. The criteria presented are agnostic of any specific ADS technological solution and/or architectural choice, to support broad implementation by others in the industry. While intended to support the readiness evaluation for the deployment of an SAE Level 4 ADS, their use can also be generalized for lower levels of automation and combined with the unique human interaction challenges applicable to those levels. Following the presentation of the proposed criteria, the article continues with a discussion on governance and decision-making toward approval of a new release candidate for the ADS, inclusive of a discussion on factors that affect residual risk and risk management practices. The implementation of the presented criteria requires the existence of appropriate safety management practices in addition to many other cultural, procedural, and operational considerations. As such, the article is concluded by a statement of limitations for those wishing to replicate part or all of its content. The content presented here serves to inform important ongoing conversations on the topic of ADS certification and the standardization of approval guidelines in international regulatory contexts.</div>

<div>This study presents a structured evaluation framework for reasonably foreseeable misuse in automated driving systems (ADS), grounded in the ISO 21448 Safety of the Intended Functionality (SOTIF) lifecycle. Although SOTIF emphasizes risks that arise from system limitations and user behavior, the standard lacks concrete guidance for validating misuse scenarios in practice.</div> <div>To address this gap, we propose an end-to-end methodology that integrates four components: (1) hazard modeling via system–theoretic process analysis (STPA), (2) probabilistic risk quantification through numerical simulation, (3) verification using high-fidelity simulation, and (4) empirical validation via driver-in-the-loop system (DILS) experiments. Each component is aligned with specific SOTIF clauses to ensure lifecycle compliance.</div> <div>We apply this framework to a case of driver overreliance on automated emergency braking (AEB) at high speeds—a condition where system intervention is intentionally suppressed. Initial numerical analysis suggested that the scenario narrowly satisfies the acceptance criteria. Applying the proposed framework to this scenario reveals that significant safety risks can persist even when the system functions according to its design intent.</div> <div>Our findings demonstrate that foreseeable misuse can be formally modeled, simulated, and empirically validated within the SOTIF framework. The proposed approach enables system developers to quantify behavioral risk and assess human-centered edge cases with greater rigor. This work contributes to operationalizing SOTIF for behavioral safety assurance and lays the foundation for future research on risk mitigation through adaptive HMI and context-aware alerts.</div>

<div>The efficient tracking and management of goods within light commercial vehicles (LCVs) is crucial for various industries, particularly craftsmen and parcel delivery services. This article explores the integration of artificial intelligence (AI) and sensor technologies to enhance item tracking and optimize logistical operations in LCVs. Two technological approaches are examined: a Bluetooth-based tracking system and a camera-based parcel identification framework. The Bluetooth-based solution is designed primarily for craftsmen. It employs Bluetooth tags, vehicle connectivity gateways (VCGs), and a centralized server to provide real-time inventory monitoring and prevent tool misplacement. The camera-based system is aimed at parcel carriers. It utilizes AI-driven object detection and pose estimation to localize and identify parcels within the vehicle. Experimental evaluations show that Bluetooth tracking ensures reliability in tool management and the AI-based vision system holds promise for future scalability in parcel logistics. The findings underscore the need for adaptive tracking methodologies to improve efficiency, reduce operational costs, and support the digital transformation of commercial vehicle ecosystems.</div>

<div>2024–2025 Reviewers</div>

<div>This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.</div>

<div>In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction using short-horizon kinematic cues. The publicly employable Lyft Level 5 dataset has been used to validate the proposed method, indicating its efficacy in attaining high prediction accuracy with low latency. The experimental results illustrate that the accuracy, precision, root mean square error (RMSE), and mean absolute error (MAE) are improved by 4.1%, 2.66%, 11.9%, and 13.3%, respectively, when the performance of the enhanced algorithm is compared to that of the YOLOv11 combined with extended Kalman filter (EKF) algorithm. Integrating YOLOv11 with the UKF leads to enhanced responsiveness and reliability of vehicle trajectory predictions, which is profitable for autonomous vehicles and advanced driver-assistance systems.</div>

<div>Four-wheel independent steering four-wheel independent drive electric vehicles have an independent steering motor and an independent driving motor for each wheel, for a total of eight motors. About 28 works in this emerging field have shown path-tracking control algorithms for these vehicles, 18 of them explicitly or implicitly aspire for a condition known as optimal tire usage. This article first defines this optimality condition and explains its significance. Second, this article identifies three indicators of tire usage that aid in assessing the existing algorithms. Third, this article performs block diagram examination of four of the 18 works, revealing significant commonalities across the 28 works and identifying areas for improvement in three of the four algorithms. Lastly, this article suggests motor control systems to fill these gaps. Furthermore, it employs these motor control systems in one of the four algorithms, and illustrates path-tracking and achievement of the optimality condition in simulation. This shows that the motor control systems are sufficient (in simulation) to meet the optimality condition in one work. They are likely sufficient for all path-tracking algorithms employing appropriate control allocation to achieve the optimality condition.</div>

<div>To mitigate traffic oscillation in mixed traffic flow environments, which reduces road capacity and may lead to traffic accidents, this article innovatively proposes a periodic-configuration vehicular platoon to enhance traffic stability, inspired by the vibration attenuation properties of periodic structures. First, the vehicular platoon model is developed based on the periodic structure principle, and the lumped mass method is applied to derive the platoon spacing transfer matrix. Second, the band gap range is calculated based on the common traffic oscillation frequency by appropriately designing the period parameters in the periodic-configuration vehicular platoon. Additionally, the influence of these period parameters on the band gap range is analyzed. Finally, simulation experiments are conducted to analyze the propagation characteristics of traffic oscillations within the platoon, and the relative position diagrams of vehicles in the platoon are obtained. To validate the effectiveness of the periodic-configuration vehicular platoon in mitigating traffic oscillations, a comparative analysis of traffic oscillation suppression is performed between periodic and non-periodic-configuration platoons. The results indicate that, for a vehicular platoon consisting of twenty vehicles, the proposed periodic-configuration platoon can suppress the propagation of traffic oscillations, and the suppression effect is up to 65%. The periodic-configuration vehicular platoon can adjust control parameters for specific frequencies of traffic oscillations to achieve improved traffic flow.</div>

<div>Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate fault tree analysis (FTA) for various scenarios and features pertaining to autonomous driving. This article is an attempt to explore the scope of using generative artificial intelligence (GenAI) FTA with the use case of malfunction for the LIDAR sensor in mind. We explore various available open source large language models (LLM) models and then dive deep into one of them to study its responses and provide our analysis. Although the article does not solve the entire problem but has given some guidance or thoughts/results to explore the possibility to train existing LLM through prompt engineering for FTA for any autonomy use case aided with PlantUML tool.</div>