Search Results (77)

The advancement of Automotive Industry 4.0 has promoted the development of Vehicle to Vehicle (V2V) and Internet of Vehicles (IoV) communication, which marks the new era for intelligent, connected and automated transportation. Despite the benefits of this metamorphosis in terms of effectiveness and convenience, new obstacles to safety, inter-connectivity, and cybersecurity emerge. The tire pressure monitoring system (TPMS) is one prominent feature that senses tire pressure, which is closely related to vehicle stability, braking performance and fuel efficiency. However, the majority of TPMSs currently in use are based on the use of insecure and proprietary wireless communication links that can be breached by attackers so as to interfere with not only tire pressure readings but also sensor data manipulation. For this purpose, we design a secure TPMS architecture suitable for real-time IoV sensing. The framework is experimentally implemented using a Raspberry Pi 3B+ (Raspberry Pi Ltd., Cambridge, UK) as an independent autonomous control unit (ACU), interfaced with vehicular pressure sensors and a LoRa SX1278 (Semtech Corporation, Camarillo, CA, USA) module to support low-power, long-range communication. The gathered sensor data are encrypted, their integrity checked, source authenticated by lightweight cryptographic algorithms and sent to a secure server locally. To validate this approach, we show a three-node exhibition where Node A (raw data and tampered copy), B (unprotected copy) and C (secure auditor equipped with alerting of tampering and weekly rotation of the ID) realize detection of physical level threats at top speeds. The validated datasets are further enriched in a MATLAB R2024a simulator by replicating the data of one vehicle by 100 virtual vehicles communicating using over 5G, LoRaWAN and LoRa P2P as communication protocols under urban, rural and hill-station scenarios. The presented statistics show that, despite 5G ultra-low latency, LoRa P2P consistently provides better reliability and energy efficiency and is more resistant to attacks in the presence of various terrains. Considering the lack of private vehicular 5G infrastructure and the regulatory restrictions, this work simulated and evaluated the performance of 5G communication, while LoRa-based communication was experimentally validated with a hardware prototype. The results underline the trade-offs among LoRa P2P and an infrastructure-based uplink 5G mode, when under some specific simulation conditions, as opposed to claiming superiority over all 5G modes. In conclusion, the presented Raspberry Pi–MATLAB hybrid solution proves to be an effective and scalable approach to secure TPMS in IoV settings, intersecting real-world sensing with large-scale network simulation, thus enabling safer and smarter next-generation vehicular systems. Full article

The automatic emergency braking (AEB) system plays a crucial role in reducing rear-end collisions and is mandatory on certain heavy-duty vehicles, with future regulations extending to passenger cars. However, most current AEB systems are designed based on onboard sensors such as cameras and radar, which may fail to prevent collisions in scenarios where the lead vehicle is already in a collision. To address this issue, this study proposes an enhanced AEB control method based on Vehicle-to-Vehicle (V2V) communication and onboard sensors. The method utilizes V2V communication and onboard sensors to predict obstacles ahead, applying effective braking when necessary. Simulation results in Matlab/Simulink R2022a show that the proposed V2V-based AEB control method reduces the risk of chain collisions, ensuring that the ego vehicle can avoid rear-end collisions even when the lead vehicle is involved in a crash. Three simulation scenarios were designed, where both the subject vehicle and the lead vehicle travel at 120 km/h. The following three distances between the subject vehicle and the lead vehicle were considered: 45 m, 70 m, and 30 m. When the lead vehicle detects an obstacle 30 m ahead and suddenly applies emergency braking, the lead vehicle fails to avoid a collision. In this case, the subject vehicle, equipped only with onboard sensors, is also unable to successfully avoid the crash. However, when the subject vehicle is equipped with both onboard sensors and vehicle-to-vehicle communication, it can prevent a rear-end collision with the lead vehicle, maintaining a vehicle-to-vehicle distance of 1 m, 6.8 m, and 3.1 m, respectively, during the stopping process. This control method contributes to advancing the active safety technologies of autonomous vehicles. Full article

Predicting potential collisions with leading vehicles is a fundamental capability of autonomous and assisted driving systems. In particular, automatic emergency braking (AEB) demands reaction times on the order of microseconds. A key limitation of existing approaches lies in their update rate, which is constrained by the sampling speed of conventional sensors. Event-based Dynamic Vision Sensors (DVSs), with their microsecond temporal resolution and high dynamic range, offer a promising alternative to frame-based cameras in challenging driving environments. In this work, we investigate the integration of DVS into autonomous driving pipelines, focusing specifically on AEB scenarios. Building on our earlier work, where a YOLO-based detection model was trained on real-world DVS data, we extend the approach to CARLA’s simulated DVS environment. We publish a CARLA-compatible 2-channel DVS dataset aligned with our detection model, bridging the gap between real-world recordings and simulation. Through a series of simulated AEB scenarios, we demonstrate how DVS enables earlier and more reliable detection compared to RGB cameras, resulting in improved braking performance. Full article

This study advances prior work on the remote control of Advanced Driver Assistance Systems (ADASs) by introducing a full manual teleoperation mode that enables remote control over both longitudinal and lateral vehicle dynamics via accelerator, brake, and steering inputs. The core contribution is a flexible, dual-mode teleoperation architecture that allows seamless switching between assisted ADAS control and full manual operation, depending on driving context or system limitations. While teleoperation has been explored primarily for autonomous fallback or direct remote driving, few existing systems integrate dynamic mode-switching in a unified, real-time control framework. Our system leverages a wireless game controller and a Robot Operating System (ROS)-based vehicle software stack to translate remote human inputs into low-latency vehicle actions, supporting robust and adaptable remote driving. This design maintains a human-in-the-loop approach, offering improved responsiveness in complex environments, edge-case scenarios, or during autonomous system fallback. The proposed solution extends the applicability of teleoperation to a broader range of use cases, including remote assistance, fleet management, and emergency response. Its novelty lies in the integration of dual-mode teleoperation within a modular architecture, bridging the gap between ADAS-enhanced autonomy and full remote manual control. Full article

To address the growing need for field calibration of the optical properties of pedestrian targets used in autonomous emergency braking (AEB) tests, a novel three-dimensional multi-faceted standard body (TDMFSB) was developed. A camera-based analytical algorithm was proposed to evaluate the bidirectional reflectance distribution function (BRDF) characteristics of pedestrian targets. Additionally, a field calibration method applied in AEB testing scenarios (CPFAO and CPLA protocols) on one new and one aged typical pedestrian target of the same type revealed a 21% decrease in the BRDF uniformity of the aged target compared to the new one, confirming optical degradation due to repeated “crash–scatter–reassembly” cycles. The surface wear of the aged target on the side facing the vehicle produced a smoother surface, increasing its BRDF magnitude by 25% compared to the new target and making it easily detectable by the vehicle’s perception system. This led to “reverse scoring,” a safety risk in performance evaluation, necessitating timely calibration of AEB pedestrian targets to ensure reliable test results. The findings provide valuable insights into the development of regulatory techniques, evaluation standards, and technical specifications for test targets and offer a practical path toward full-life-cycle traceability and quality control. Full article

As electric vehicles (EVs) continue to advance toward widespread adoption, innovations in power electronics are playing a pivotal role in improving efficiency, performance, and sustainability. This review presents recent progress in bidirectional converters and regenerative braking systems (RBSs), highlighting their contributions to energy recovery, battery longevity, and vehicle-to-grid integration. Bidirectional converters support two-way energy flow, enabling efficient regenerative braking and advanced charging capabilities. The integration of wide-bandgap semiconductors, such as silicon carbide and gallium nitride, further enhances power density and thermal performance. The paper evaluates various converter topologies, including single-stage and multi-stage architectures, and assesses their suitability for high-voltage EV platforms. Intelligent control strategies, including fuzzy logic, neural networks, and sliding mode control, are discussed for optimizing braking force and maximizing energy recuperation. In addition, the paper explores the influence of regenerative braking on battery degradation and presents hybrid energy storage systems and AI-based methods as mitigation strategies. Special emphasis is placed on the integration of RBSs in advanced electric vehicle platforms, including autonomous systems. The review concludes by identifying current challenges, emerging trends, and key design considerations to inform future research and practical implementation in electric vehicle energy systems. Full article

There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article

Autonomous vehicles are increasingly being adopted, with manufacturers competing to enhance automation capabilities. While full automation eliminates human input, lower levels still require driver intervention under specific conditions. This study presents the design and development of a prototype vehicle featuring both low- and high-level control systems, integrated with a 5G-based teleoperation interface that enables seamless switching between autonomous and remote-control modes. The system includes a malfunction surveillance unit that monitors communication latency and obstacle conditions, triggering a hardware-based emergency braking mechanism when safety thresholds are exceeded. Field experiments conducted over four test phases around Chulalongkorn University demonstrated stable performance under both driving modes. Mean lateral deviations ranged from 0.19 m to 0.33 m, with maximum deviations up to 0.88 m. Average end-to-end latency was 109.7 ms, with worst-case spikes of 316.6 ms. The emergency fallback system successfully identified all predefined fault conditions and responded with timely braking. Latency-aware stopping analysis showed an increase in braking distance from 1.42 m to 2.37 m at 3 m/s. In scenarios with extreme latency (>500 ms), the system required operator steering input or fallback to autonomous mode to avoid obstacles. These results confirm the platform’s effectiveness in real-world teleoperation over public 5G networks and its potential scalability for broader deployment. Full article

The objective of this study was to analyze the effects of variables such as pre-crash emergency braking and reclined posture on human injuries in autonomous vehicle collisions using an active human model and through crash analysis. To achieve this, the MADYMO (MAthematical DYnamic MOdels) active human model was validated for predicting occupant responses during pre-crash emergency braking. Its biofidelity during crash conditions was also validated. Additionally, the model was validated under component-level impact conditions to ensure its suitability for predicting occupant injuries. Two autonomous vehicle-relevant crash scenarios reconstructed based on actual accident conditions were selected. Variations in collision conditions, such as collision angles, overlaps, and relative collision speeds, were applied to selected crash scenarios. A finite element vehicle-to-vehicle crash analysis was performed to obtain the crash pulse. Using the validated crash analysis model, a parametric simulation study was conducted by applying variations to parameters such as emergency braking, seat-related parameters, and muscle activity. Finally, the impact of each variable on injury risk was analyzed using the Wilcoxon rank sum test. Analysis results showed that a reclined posture and a seat track position located 300 mm rearward from the baseline seat track position had a significant impact on injuries. Evaluation results on the effects of these variables can contribute to the development of safety evaluation standards for autonomous vehicles, such as crash safety regulations, by crash safety assessment organizations. Full article

The need for autonomous vehicles has started rising rapidly. Many autonomous technologies, such as Cruise Control, the self-parking system, and the emergency braking system, are implemented in contemporary cars. These systems do not make the car fully autonomous; however, they allow people to get used to the idea of self-driving cars. Due to a surge of interest in autonomous systems, the development of these technologies has begun. This paper presents a model of Adaptive Cruise Control with a control system, which consists of two PID regulators. Using two PID regulators provides the possibility of more advanced regulation characteristics than using the classical one-PID regulation system. One of them regulates the powertrain model, the other the braking system model. The simulations are carried out using a vehicle dynamic system, whose thrust is determined by a real engine maximum torque curve that is approximated by combinations of polynomial functions. Due to the non-linearity, caused by the motor’s curve and the use of two regulators, the PID tuning algorithm has been created. The algorithm provides satisfying results, followed by a marginal difference between the requested safe distance and actual distance value. The Active Cruise Control system has been tested using normalized driving cycles, which simulate the real behaviour of a car. The simulation results prove double-PID-regulated ACC’s accuracy and speed of response in different states of motion. Full article

Seatbelts are critical components of vehicle safety, continuously evolving through technological advancements and regulatory updates. Traditionally designed to secure occupants during collisions, seatbelt innovations, such as retractors, pretensioners, and load limiters, have significantly enhanced comfort and effectiveness. With the advent of autonomous vehicles, seatbelt systems must adapt to new safety challenges, including real-time tension adjustment through active seatbelt systems. These systems, integrated with active safety technologies like automatic emergency braking, offer a more comprehensive safety solution. Furthermore, seatbelt technology must address the diverse needs of different passenger categories. Quantitative data highlight the role of seatbelts for various passenger categories. Children are 55% more likely to be injured by rear structure intrusion and 27% more likely to suffer from compression into the front seat during rear impacts. Pregnant women generally experience milder injuries but are more prone to abdominal injuries. Older adults, who account for 17% of crash fatalities, are more likely to suffer thoracic injuries and fractures due to increased bone fragility. This review explores the integration of traditional and modern seatbelt systems, focusing on passenger-specific adaptations and the future role of seatbelts in autonomous vehicles. This study is based on a thorough literature review, analyzing data from the Web of Science, Scopus, and SAE databases, where available, to assess the contributions and impact of these innovations. Full article

With the continuous development of assisted driving technology, the autonomous emergency braking (AEB) system has emerged as a critical innovation in preventing collisions and improving vehicular safety. In this paper, to test the performance of the AEB system efficiently and reliably in real-world driving scenarios, four typical test scenarios for the AEB system were constructed, and five comprehensive performance evaluation indices, including braking parking distance, braking deceleration, collision warning time, speed variation, and accident collision avoidance rate, were proposed for the first time. Subsequently, the Comprehensive Performance Evaluation Index Model (CPEIM) for the AEB system and scoring rules for typical test scenarios were established, which were applied to analyze data obtained from road testing, thereby enabling comprehensive testing and evaluation for AEB system performance. The results showed that the Tesla Model Y and Volvo S90 scored 1.8857 and 2.0433, respectively. Under conditions of dry pavement, across a range of test scenarios, the AEB system of both the Tesla Model Y and Volvo S90 were capable of averting collisions at speeds not exceeding 35 km/h and 45 km/h, respectively. Full article

The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios. Full article

The automated guided vehicle (AGV) is widely used in industrial environments for goods transportation. However, issues such as mechanical wear, reduced battery life, navigation error accumulation, and decreased operational efficiency caused by frequent starts and stops need to be addressed. This paper proposes an improved Autonomous Emergency Braking (AEB) algorithm to tackle these problems. The algorithm employs a stepwise deceleration strategy, effectively reducing the frequency of sudden stops and enhancing the system’s operational smoothness. The AEB algorithm not only considers straight-line driving scenarios but also optimizes deceleration strategies for turning scenarios, adjusting the deceleration detection range according to the turning trajectory. Additionally, a velocity smoothing algorithm is designed to ensure that speed changes during deceleration are gradual, avoiding abrupt speed variations that could impact the system. The feasibility of the AEB algorithm is validated through testing on actual equipment, and its performance is compared to that of a conventional emergency stop strategy. Experimental results show that the AEB algorithm significantly reduces the number of sudden stops, improves the AGV’s operational smoothness and safety, and demonstrates excellent adaptability and robustness across different operational conditions. Full article

Safe driving and effective collision avoidance are critical challenges in the development of autonomous driving technology. As the dynamic interactions between vehicles and pedestrians become increasingly complex, making rational decisions and accurately executing planning and control in emergency situations has become a core issue for sustainable development relating to traffic mobility and safety. This paper proposes an active collision-avoidance control strategy based on emergency decisions and planning in the context of vehicle–pedestrian interactions. A safety-distance model is developed with consideration given to the dynamic interactions between these two entities, and an emergency-decision mechanism is designed using the integration of priority rules. To generate smooth collision-avoidance trajectories, a quintic polynomial method is employed to construct trajectory clusters that meet the desired specifications. Moreover, a multi-objective optimization value function which considers multiple factors comprehensively is used to select the optimal path. To enhance collision-avoidance control accuracy, an RBF (radial basis function)–optimized SMC (sliding mode control) algorithm is introduced. Additionally, an FD-SF (force demand–based speed feedback) algorithm is designed to accurately track the longitudinal braking path. The results indicate that the proposed strategy can generate efficient, comfortable, and smooth optimal collision-avoidance paths, significantly improving vehicle response speed and control accuracy. Full article