Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Vehicular Ad Hoc Networks (VANETs) are one of the key components for intelligent transportation systems but are still highly vulnerable to dangerous security abuses. Beyond conventional intelligent transportation systems, such secure vehicular communication is also highly relevant to smart mining environments, where autonomous haul trucks, connected service vehicles, and roadside infrastructure must exchange safety-critical messages reliably under adversarial conditions. In a blackhole attack, malicious nodes drop data packets. In this paper, we propose a new intrusion detection model based on multi-metric trust evaluation with the Random Forest (RF) machine learning algorithm. NS-3 simulation is used to generate a realistic dataset that contains five key performance indicators (KPIs) (Packet Delivery Ratio [PDR], End-to-End [E2E] Delay, Jitter, Collision Rate [CR], and Route Stability [RS]). In this model, finally RF classifier is used to identify and then quarantine malicious nodes dynamically. The experimental results show that the proposed approach significantly outperforms baseline AODV routing protocol and a recently proposed trust-based scheme. The model can maintain the PDR greater than 92% and average delay less than 42 ms, with a detection accuracy of greater than 96.3%, under the scenario with more than 30% malicious nodes as well as low false-positive rate (<2.5%). These results confirm that a multi-dimensional trust assessment with ensemble machine learning technique yields a strong and scalable solution to improve the security, safety, and reliability of VANETs....
The Intelligent Driver Model (IDM), while effective in simulating car-following dynamics for autonomous vehicles (AVs), often produces excessive braking forces during traffic light stops, compromising passenger comfort and energy efficiency. This paper introduces the Blended Acceleration Model (BAM), a novel framework that integrates IDM’s acceleration dynamics with a virtual deceleration function regulated by a dynamic blend factor. BAM adaptively adjusts the deceleration phase based on real-time distance to the traffic light, ensuring smooth transitions between acceleration and braking to mitigate abrupt maneuvers. Tested in a single-traffic-light scenario with a 100 m detection range, BAM combines IDM’s responsiveness with a gradient-based deceleration strategy inspired by stepwise velocity control in automated guided vehicles. The blend threshold factor (α = 10) optimally balances comfort and performance, maintaining deceleration within the comfortable range (≤ 1.5 m/s²) at typical urban speeds (≤ 60 km/h). Simulation results show that BAM achieves a 24.6% reduction in peak deceleration and a 30.6% shorter stopping time compared to IDM, while maintaining well-damped, monotonic deceleration without oscillations. Jerk profiles remain smooth and stable, with temporary peaks occurring only during final braking phases and within acceptable comfort limits. Compared to IDM and the Optimal Velocity Model (OVM), BAM delivers superior braking comfort, smoothness, and stopping accuracy, while OVM, though quicker, induces harsher braking. By addressing IDM’s rigidity in deterministic stopping scenarios, BAM enhances both passenger comfort and operational efficiency, offering strong potential for integration into urban AV control systems....
In multirotor unmanned aerial vehicle (UAV) GNSS/INS integrated navigation systems, a single filter such as the extended Kalman filter (EKF) or the error-state extended Kalman filter (ESKF) is commonly adopted. However, both methods have inherent performance limitations. The EKF suffers from significant linearization errors in highly nonlinear flight scenarios, leading to degraded estimation accuracy. Although ESKF achieves higher precision during steady flight, its model assumptions may no longer strictly hold during aggressive maneuvers, causing performance degradation in complex flight missions. To address the limitations of using a single filter, this study proposes a dynamic filter selection strategy under the interaction multi-filter (IMF) framework. The approach builds on the interactive multiple model (IMM) method and establishes a cooperative mechanism between EKF and ESKF. By computing the filter likelihoods at each time step and updating the probability switching matrix, the framework adaptively selects the optimal filter based on the current flight conditions. Simulation results demonstrate that the proposed IMFbased strategy effectively avoids the performance bottlenecks of individual filters. In highly nonlinear environments, it reduces linearization errors and suppresses divergence trends; compared with traditional ESKF, the proposed algorithm 3D RMSE is reduced by 57.2%, compared with the adaptive robust EKF (AREKF), the proposed approach reduces positioning errors by up to 21.3%. The results confirm that IMF-based adaptive switching between EKF and ESKF yields a robust, high-precision solution for UAV navigation in complex operational scenarios....
Electric vehicles (EVs) are pivotal for reducing transportation-related emissions; however, the lack of adequate charging infrastructure remains a significant barrier to their widespread adoption. This study presents a comprehensive methodology for optimizing EV charging station placement. It combines a gravity model, scenario analysis, and mixed-integer linear programming (MILP) to ensure a thorough and robust approach. The model aims to maximize accessibility by ensuring both path-level and overall system demand coverage across diverse scenarios, providing reassurance about the validity of the findings. The methodology is tested on the Bursa–˙Izmir motorway in Turkey, a strategic intercity route with rapidly growing EV penetration. Results reveal that the optimal configuration involves locating charging stations in seven of the nine service areas. This allocation secures a minimum path coverage ratio of 0.903, meaning 90.3% of the route is covered by charging stations, and an overall demand coverage ratio of 0.935, indicating that 93.5% of total demand is covered across all scenarios. A sensitivity analysis further shows that increasing the network to 45 chargers elevates reachability levels to above 97%, indicating the infrastructure scale required for reliable service quality. The findings underscore the practical applicability of the proposed framework, providing policymakers and infrastructure planners with robust, data-driven guidance for charging network expansion. By integrating demand forecasting with resilient optimization, this study advances both methodological and empirical insights, empowering the audience to make informed decisions for sustainable EV adoption....
The fire resistance and thermal propagation delay of a flame-retardant battery pack case (BPC) were investigated in this study for electric vehicles. Following the Lithium-ion traction battery pack and system for electric vehicles, Part 3: Safety requirements and test methods 31467.3-2015 standards, the BPC specimen was exposed to 500–600 ◦C for 15 min. Six thermocouples monitored the non-exposed surface, which reached a maximum of 149.7 ◦C, below the 150 ◦C limit. No flame occurred during or after heating, and the structure maintained integrity without cracks. The results confirm the flame-retardant BPC’s excellent thermal shielding and demonstrate its potential to enhance EV battery safety by delaying heat transfer and preventing secondary ignition....
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