Vehicles

Automotive thermal-comfort studies commonly focus on interactions between occupants and cabin air, although the seat forms the principal sustained contact interface between an occupant and a vehicle. This study presents a coupled computational fluid dynamics (CFD) framework that simultaneously resolves heat conduction through perforated leather, breathable sponge, heating pads, and foam; porous airflow through the seat; seat heating and ventilation; and convective heat transfer between the occupant and cabin air. Four total seat-heating powers (0, 60, 90, and 120 W) and four ventilation-fan speeds (0, 1500, 3000, and 4500 rpm) were simulated for 900 s. The results show that seat heating primarily alters temperatures in the contact region through conduction, whereas seat ventilation increases local airflow and cooling near the edges of ventilated contact regions. The principal contribution is a reproducible interface between cabin CFD and thermophysiological modeling. The framework generates spatially resolved fields of air temperature, air velocity, skin and contact-surface temperatures, and heat flux that can serve as boundary conditions for physiological models such as the Fiala and Berkeley models and for subsequent experimental validation. Because the present calculations constitute a comparative single-driver study, direct experimental validation, active thermoregulation, subjective thermal sensation, and interactions under full occupancy must be addressed before the framework can provide absolute comfort predictions. Full article

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $\mathrm{150,000}$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article

Nowadays, the automotive industry is facing an increasing demand for comfort, handling, and stability; thus, the suspension system plays a crucial role in ensuring these modern requirements. The growing need for improved dampers requires a deeper understanding of how shock absorbers behave under certain conditions. Consequently, experimental analysis of damping systems represents an essential step in the development process of an advanced and modern suspension system. This paper aims to present an experimental investigation of shock absorber behaviour using a dedicated testing stand designed to evaluate the damping phenomenon and how it may be affected under variable operating conditions. The current study focuses on the analysis of the force–stroke relationship by modifying key excitation parameters such as frequency and amplitude. The tests will be performed for different stroke amplitudes and frequencies ranging from 0.3 Hz to 1 Hz. The experimental setup can be used on either standard or adaptive shock absorbers. It allows for controlled variation of input signals, thus enabling the observation of the dynamic response and how it varies over time. The resulting diagrams provide insights into the energy dissipation process and into the hysteresis phenomenon that occurs while testing. Also, a mathematical model developed in AMESim will be used to compare the results obtained with both experimental and numerical methods. The study contributes to a better understanding of shock absorber behaviour, helping to build a foundation for future developments involving control strategies for adaptive suspension systems. Full article

The Euro 7 standard for Heavy-Duty (HD) vehicles introduces stricter Solid Particle Number (SPN) emission limits by reducing In-Service Conformity (ISC) thresholds from 9.8 × 10¹¹ #/kWh to 9.0 × 10¹¹ #/kWh and lowering the minimum detectable particle size from 23 nm (SPN₂₃) to 10 nm (SPN₁₀). SPN emissions are excluded from active regeneration of Diesel Particulate Filters (DPFs), but passive regeneration during ISC testing remains a regulated condition for HD vehicles. These stricter regulations underscore the pressing need for advanced DPF technologies to ensure compliance under real-world operating conditions. In this study, a Euro VI Step E HD vehicle was evaluated on an HD chassis dynamometer using a predefined real-world ISC testing route to assess SPN emissions during passive regeneration of soot-loaded DPFs. Two SPN instruments, one downstream of the DPF and one downstream of the subsequent Selective Catalytic Reduction (SCR), allowed for a parallel characterization of the DPF performance and the net SPN increase observed across the SCR system. The tests indicate that passive regeneration in Euro VI vehicles can lead to SPN₁₀ emissions as high as 23 × 10¹¹ #/kWh with current technology DPFs. However, the advanced DPF technology limited weighted DPF-out SPN₁₀ emissions to 1.2 × 10¹¹ #/kWh, below the net increase observed across the SCR system (2.3 × 10¹¹ to 2.9 × 10¹¹ #/kWh), even during passive regeneration events, presenting a feasible pathway for achieving compliance with future emission standards. Full article

Modern vehicles feature an increasing number of automated driving functions. This raises formal safety requirements for systems that directly intervene with these functions, such as steering or braking systems. The state-of-the-art offers various solutions that ensure fail-operational capability of safety-relevant systems. However, many existing approaches are associated with increased material and integration effort, particularly when applied to existing automotive E/E architectures. This paper proposes a capacitor-based Safety Adapter to address this gap. It is intended to support the fulfillment of higher functional safety requirements under ISO 26262. We derive formal safety requirements based on an exemplary steering system and also present a first possible hardware layout. Initial verification results on a HiL test bench indicate that the proposed Safety Adapter can maintain steering assistance during undervoltage scenarios, even under aged conditions and very low ambient temperatures. Full article

The transient pressure rise in occupants’ ears during vehicle door closure remains a key challenge for cabin comfort, but existing simulation methods often lack accuracy or efficiency. This study develops an integrated experimental–CFD–theoretical framework. A high-fidelity vehicle model was constructed from point cloud data and validated against airtightness and door-closure tests. A theoretical model was derived and calibrated using flow hysteresis and fluctuation attenuation coefficients from CFD results. Uncontrolled leakage was represented by distributed circular holes, and the one-way flow through the pressure relief valve was implemented numerically. The refined CFD model reduced the peak-pressure and amplitude errors from 6.33% and 17.62% to 1.75% and 2.66%, respectively. The calibrated theoretical model achieved 93.94% accuracy in pressure amplitude relative to the CFD results, with much lower computational cost. An optimization strategy combining early valve opening with an auxiliary fan at the relief valve reduced peak pressure, amplitude, and pressure change rate by 25.88%, 22.83%, and 41.23%, respectively. By deeply integrating experiments, simulation, and theory with refined modeling of key physical features, this research overcomes the accuracy and efficiency limitations of traditional methods, offering a systematic solution for cabin comfort optimization and advancing forward-development capabilities in vehicle NVH. Full article

Autonomous driving systems often exhibit trajectory tracking behavior that differs markedly from human drivers, a phenomenon intuitively described as “mechanicalness.” This study moves beyond the traditional focus on tracking accuracy to systematically analyze these behavioral differences through a real-vehicle comparative experiment. Using a steer-by-wire vehicle equipped with the open-source Autoware platform, trajectory data were collected on a closed campus road under straight and curved conditions. A five-dimensional evaluation framework is established to quantify control continuity, prediction horizon, error response mode, style adaptability, and interaction friendliness. Results show that Autoware exhibits a “high-precision, low-smoothness, zero-tolerance” mechanical style, characterized by high-frequency micro-corrections, reactive curve entry, rigid speed tracking, and segmented braking. Human drivers, in contrast, employ an organic mode with discrete corrections, elastic path tolerance, and anticipatory coordination. The technical origins of mechanicalness are identified as four algorithmic paradigms: geometric tracking, decoupled control, error-driven logic, and limited prediction horizon. The findings further reveal a systematic safety–comfort trade-off inherent to mechanical control, and suggest optimization directions including adaptive dead-zone mechanisms, extended prediction horizons, and lateral–longitudinal coordination. These insights provide theoretical and engineering foundations for developing more human-like autonomous driving control strategies. Full article

Brake pad testing has attracted increasing scientific attention in recent years, particularly in focus of the upcoming EURO 7 regulation, which will introduce for the first time limits on PM₁₀ emissions from tires and brakes. This study aims to compare the friction behavior determined using a Pin on Disc setup with that obtained from an inertia dynamometer test setup. For this purpose, a conventional low metallic brake pad material was tested with a ductile cast iron rotor. The test procedure was based on selected sections of the SAE J2522 standard (AK Master). The scaling method applied to the Pin on Disc system, as presented in this study, enables the calculation of deceleration based on the energy per contact area. After the Burnish section, the Pin on Disc setup systematically achieved a higher mean coefficient of friction of approximately 2 to 6% across the Speed/Pressure Sensitivity sections, with a larger offset of about 14% above 160 km/h. Meanwhile, the average coefficient of friction for the dynamometer test generally declined with speed, but on the Pin on Disc setup it only decreased up to 120 km/h. The coefficient of friction in Pin on Disc tests also decreased as pressure increased. SEM and EDX analysis showed the formation of bright primary metallic plateaus and darker, oxidized secondary plateaus. The disc tracks exhibited carbon- and oxygen-enriched tribological layers. Full article

Railroad daily operations can be significantly disrupted by unforeseen events such as adverse weather conditions and train accidents. These events often result in delays in delivering shipments on time. Therefore, studying how to reschedule freight trains during emergencies is essential for enhancing the competitiveness of rail transportation products. This paper proposed a method specifically designed for the rescheduling of freight trains, aiming to simultaneously modify the train operation plan and dynamically adjust railcar flow routes in response to unexpected events. The problem was formulated with a multi-objective model based on a two-layer space–time network. To accurately measure schedule deviations, we introduced a set of indicators specifically tailored to the characteristics of railroad freight transportation. The ε-constraint method was employed to establish a framework for searching three-dimensional Pareto solutions. Computational experiments were performed on a Chinese railroad network. The results validated the effectiveness and applicability of the proposed methodology. Full article

Lane-Keeping Assist Systems (LKAS) are widely used in modern passenger vehicles to support lateral vehicle control and reduce the risk of unintended lane departure. Although LKAS performance is commonly evaluated in relation to perception, control, and sensor fusion, the observable vehicle response may also depend on the mechanical condition of the chassis. This study presents a qualitative, measurement-based evaluation of the influence of intentionally introduced front-wheel toe misalignment on the observable response of a production LKAS under controlled proving-ground conditions. Experimental tests were conducted on the highway module of the ZalaZONE proving ground using a Lexus RX 450h equipped with a factory-installed LKAS function. Three front-wheel toe configurations were investigated: factory-specified alignment, single-wheel toe misalignment, and severe toe misalignment affecting both front wheels. Measurements were performed at 70, 90, and 110 km/h on straight and curved road sections. Vehicle speed, steering angle, lateral acceleration, and GNSS-based position data were recorded using CAN- and GNSS/IMU-based data acquisition. The qualitative comparison of the measured signal profiles indicated that the misaligned configurations were associated with a shifted steering-angle operating range and less uniform steering and lateral-acceleration responses. The most pronounced visible differences occurred under the severe toe-misalignment condition, particularly at higher speeds and in the curved section. As the analysis did not include quantitative effect measures or statistical comparisons, these observations are interpreted as exploratory tendencies rather than statistically validated changes in LKAS performance. The findings suggest that front-wheel toe condition should be considered in the measurement-based assessment, maintenance, and calibration of ADAS-equipped vehicles. Full article

This study investigates a simulation-based estimation–control chain that integrates an interactive multiple model adaptive unscented Kalman filter (IMM-AUKF) with a multiple-input multiple-output model predictive controller (MIMO-MPC). A seven-degree-of-freedom vehicle model and a Pacejka tire model are used to represent nonlinear vehicle dynamics. The controller updates model and regression region steering constraints from the estimated adhesion state and jointly allocates front/rear steering and longitudinal force commands. The revised experiments use a 0.5 ms plant integration step and a consistent 20 ms estimator/controller update. Under high-adhesion double lane change (DLC), the proposed chain lowers speed RMSE from 0.7950 to 0.1833 m/s and mean adhesion estimation RMSE from 0.1567 to 0.0812. Under variable-adhesion single lane change (SLC), lateral RMSE decreases from 0.1816 to 0.1649 m, speed RMSE from 0.7983 to 0.2505 m/s, and mean adhesion estimation RMSE from 0.1622 to 0.0949. Heading error is not uniformly improved and is reported as a design trade-off. These results provide reproducible simulation evidence, while hardware-in-the-loop and real-vehicle validation remain future work. Full article

Fuel cell electric vehicles (FCEVs) require energy management strategies that can balance hydrogen economy, battery utilization, component protection, and real-time control under varying driving conditions. This paper proposes an Adaptive-Modality Deep Deterministic Policy Gradient and Model Predictive Control hierarchical energy management strategy (AMDDPG–MPC HEMS). In the proposed architecture, the upper-level AMDDPG controller identifies driving-condition patterns and generates adaptive weights for hydrogen consumption, battery power, and state-of-charge regulation, while the lower-level MPC controller performs constrained power allocation between the fuel cell and battery. To improve adaptability, the AMDDPG algorithm incorporates an adaptive modality perception mechanism that extracts driving-condition features and a multi-scale reward mechanism that coordinates short-term energy-saving objectives with long-term component-protection requirements. A dedicated weight-scheduling and switching mechanism is also introduced to ensure smooth transitions between operating conditions. The proposed strategy is evaluated under the World Light Vehicle Test Cycle and Urban Dynamometer Driving Schedule and compared with rule-based, equivalent consumption minimization, and fixed-weight MPC strategies. The results show that the AMDDPG–MPC HEMS achieves the lowest equivalent hydrogen consumption, with reductions of 18.853% and 11.732% relative to the rule-based strategy under the two driving cycles, respectively. It also improves fuel-cell operating efficiency and maintains feasible battery SOC regulation. These results demonstrate the effectiveness and engineering potential of the proposed hierarchical energy management strategy. Full article

In response to the limitations of the conventional fixed-rate pricing model for freeways in addressing spatiotemporal imbalances in traffic flow distribution and low road network resource utilization, as well as the neglect of Vehicle Collision probability in existing differentiated pricing frameworks, this study analyzes the underlying relationship between road traffic saturation and collision probability. On this basis, a bi-level programming model for differentiated freeway pricing that explicitly accounts for Vehicle Collision probability is developed. The upper-level model maximizes the economic benefit of freeway operations while incorporating vehicle-collision-probability-related costs, whereas the lower-level model minimizes vehicle travel impedance, including both travel cost and time delays induced by collisions. A hybrid algorithm combining particle swarm optimization (PSO) and pattern search (PS) is employed to solve the optimal pricing scheme across different vehicle types (passenger cars and trucks), road segments, and time periods. A nested Logit model is introduced to characterize travelers’ route choice behavior between alternative routes, thereby capturing the traffic redistribution effect induced by pricing rate adjustments. Empirical data from a section of the Shenyang–Haikou Freeway corridor in China for the year 2024 are used for model calibration and validation. The results indicate that following the optimized pricing scheme, the freeway’s monthly net revenue increased by 9.44%, truck and passenger car traffic volumes rose by 8.59% and 6.12%, respectively, and total segment traffic volume increased by 7.18%. The saturation of the freeway increased from approximately 0.18 to about 0.32, while that of the parallel arterial decreased from approximately 0.9 to about 0.67, indicating a more balanced traffic flow distribution across the network and a substantial reduction in Vehicle Collision probability. These findings demonstrate that the proposed method can effectively balance the trade-offs among operational revenue, traffic efficiency, and driving safety, providing both theoretical support and a quantitative tool for the scientific formulation of differentiated freeway pricing schemes. Full article

Surrogate safety assessment in motorcycle-dominated mixed traffic requires screening methods that distinguish threshold-sensitive event prevalence from stable site, time, and interaction patterns. This study develops a TTC–THW-based site–time–interaction framework for engineering screening using 13.342 h of video from six Hanoi arterials, comprising 371,864 vehicles, 2,977,401 validated leader–follower observations, and 180,722 TTC-defined events. Primary window-level inference uses 799 complete non-overlapping 60 s windows; 3181 overlapping windows are retained only for sensitivity analysis. Increasing the TTC reference from 1.5 to 2.4 s raised the flagged-observation proportion from 12.04% to 17.99%, without changing the site ordering. A one-standard-deviation increase in median THW was associated with a 13.81% lower expected TTC-defined event rate per following exposure in the primary model (IRR=0.8619, 95% CI 0.8433–0.8809), with similar estimates under overlapping-window and AR(1) GEE specifications. Follower-clustered event models showed stable vehicle-pair contrasts across TTC-defined criticality cut-points of 0.6, 0.8, and 1.0 s; the MC–Car odds ratios were 1.440, 1.352, and 1.273, respectively, relative to Car–Car. Alternative THW boundaries and empirical tertiles retained the main temporal-compression contrasts. Downsampling from 7.5 to approximately 3.75 Hz reduced absolute event counts by about 30.6–30.7% but left comparative site rankings unchanged. The framework therefore provides trajectory-based interaction screening rather than crash-risk prediction or independently validated near-crash classification. Full article

The use of helicopters demands very accurate management and the correct use of assets. This is crucial in achieving good performance and avoiding tasks that could be inconducive to extinguishing the fire or even cause serious accidents. In the field of civil protection, very few studies present this or a similar type of analysis for the use of helicopters. This study proposes a structured approach designed to improve the safety and operational efficiency of firefighting helicopters by integrating lessons learned from real accident case studies, supported by maintenance key performance indicators (KPI), to improve the management and use of these resources. The analyzed case studies identified recurrent operational risks associated with helicopter downwash, low-altitude operations, inadequate coordination between aerial and ground crews, and insufficient post-maintenance validation. The maintenance analysis demonstrated that an increased mean time to repair (MTTR) significantly reduces fleet availability and consequently increases reserve fleet requirements. These findings highlight the importance of integrating operational safety, maintenance management, and fleet planning to improve the reliability and readiness of aerial firefighting operations. Full article

Ammonia, as a hydrogen carrier and carbon-free alternative fuel, shows great potential in future low-carbon energy systems. This study uses dimethyl ether (DME) as a combustion promoter for ammonia to enhance the combustion performance of ammonia-fueled engines. To address the issue of unburned ammonia emissions, the original combustion chamber geometry was optimized by removing the squish area to enhance flame propagation. At an ammonia energy ratio (AER) of 60%, the modified combustion chamber (MCC) reduces unburned ammonia (uNH₃) emissions by up to 85.46% and improves indicated thermal efficiency (ITE) by 1.93% compared to the original combustion chamber (OCC). Furthermore, to achieve higher thermal efficiency and lower pollutant emissions, the DME injection strategy was redesigned based on the MCC. The results show that adjusting the single injection timing (SIT) and injection angle (INA) of DME can effectively improve the homogeneity of the in-cylinder combustible mixture and enhance combustion efficiency; however, overly concentrated injection can lead to rapid heat release and increase the risk of knock. The split injection strategy enables more controllable combustion phasing, significantly reduces the maximum pressure rise rate (MPRR) and ringing intensity (RI), and mitigates knocking tendency. When the main injection timing (MIT) is −5 °CA ATDC, pilot injection timing (PIT) is −30 °CA ATDC, and the pilot injection ratio (PIR) is 60%, the ITE reaches 49.98%, which is 3.53% higher than that of the pure diesel mode. Greenhouse gas (GHG) and NO_x emissions are reduced by 45.94% and 62.49%, respectively, with uNH₃ emissions as low as 4.16 g/kW·h. Full article

UAV visual tracking remains challenging because aerial imagery frequently contains small targets, visually similar distractors, camera motion, occlusion, and rapid appearance variation. To improve target representation and template–search matching under these conditions, we propose SiamDC, an anchor-free Siamese tracker built upon SiamCAR. SiamDC introduces a Dual-channel Collaborative Enhancement (DCE) module that jointly models spatial dependencies and inter-channel relationships within the template and search branches and further transfers branch-specific channel relationships reciprocally between them. In addition, a Cross-Correlation Feature Fusion (CFF) module is developed to perform a cascaded matching process: pixel-wise correlation first preserves fine-grained spatial correspondence, after which the correlation responses are fused with the search representation and further processed by channel-preserving depth-wise cross-correlation. Experiments on DTB70, UAV123, and UAV20L show consistent improvements over the SiamCAR baseline and competitive performance against the evaluated trackers while retaining real-time tracking capability. Under the standardized efficiency evaluation protocol, SiamDC requires 55.80 M parameters and 26.90 GFLOPs and achieves a network-forward speed of 44.1 FPS on an NVIDIA RTX 3080. Full article

The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations. Full article

This paper presents an adaptive event-triggered nonsingular fast terminal sliding-mode control (AETSMC) framework for quadrotor unmanned aerial vehicles subject to aerodynamic disturbances, actuator loss of effectiveness (LOE) of up to 60%, and intermittent denial-of-service (DoS) attacks. First, a nonsingular fast terminal sliding-mode (NFTSM) surface is constructed using fractional powers of the tracking error rather than fractional-order derivatives. This design ensures finite-time convergence while avoiding the singularity associated with conventional terminal sliding-mode schemes. Second, a smooth positive-semidefinite barrier function (Smooth-PSBF) is incorporated into the adaptive gain law. The resulting law provides only the compensation required to maintain the prescribed bound, thereby limiting gain overestimation and chattering. Third, a dual-mode event-triggering mechanism combines an exponentially decaying threshold with a zero-order hold. A positive lower bound on the inter-event interval is derived from the closed-loop dynamics, which excludes Zeno behaviour. Simulations under matched conditions show that the proposed method reduces the pitch-channel root-mean-square error by 79.4% and the integral squared error by 95.8% relative to the first reproduced baseline. In a separate 15-s communication experiment sampled at 1 kHz, the controller generated 128 transmissions instead of 15,000 periodic updates, corresponding to a 99.15% reduction. These results indicate that the proposed framework can improve fault-tolerant tracking while reducing communication demand under intermittent DoS attacks. Full article