Engineering

Abstract: High ambient temperature and repeated heat-soak exposure can increase the thermal-management burden of electric-vehicle batteries and accelerate temperature-sensitive degradation mechanisms. This study develops a coupled simulation framework combining a second-order resistor–capacitor equivalent-circuit model, state-of-charge dynamics, Bernardi-type heat generation, a two-node core–surface thermal network, proportional active battery thermal management, and Arrhenius-weighted degradation exposure. A repeatable tropical boundary condition of 28–42 \( ^{\circ}C \) is imposed together with a synthetic driving/charging duty cycle. Under the reference parameter set, active thermal management limits the peak simulated core temperature to 35.86 \( ^{\circ}C \) compared with 44.62 \( ^{\circ}C \) for passive operation, a reduction of 8.76 \( ^{\circ}C \). The three-day equivalent-cell BTMS electrical demand is 0.166 kWh and the integrated heat removal is 0.243 kWh. Active cooling reduces the normalized cumulative degradation exposure by approximately 14.0% relative to passive operation. Setpoint sweeps from 28 to 36 \( ^{\circ}C \) quantify the trade-off between cooling energy and degradation exposure. Because cell-specific aging data are not yet available, long-term results are reported as normalized degradation exposure rather than experimentally validated percentage SoH. The framework therefore provides a reproducible basis for subsequent parameter identification, experimental validation, and pack-level lifetime optimization under tropical operating conditions.

Abstract: Controller Area Network (CAN) intrusion detection across vehicles is challenging because identifiers, timing patterns, and payload encodings are vehicle-specific, while serial correlation complicates alarm calibration. This study evaluated target-specific, normal-only adaptation rather than zero-shot transfer. Benchmark labels served only to delimit verified-clean target prefixes; attack labels were excluded from feature construction, model fitting, and calibration. A 12-dimensional residual representation captured per-identifier payload and timing deviations, identifier rarity and novelty, transition surprise, and window-level distributional change. A dependence-informed empirical block-rank (EBR) procedure calibrated Isolation Forest anomaly scores at block level. Across four can-train-and-test target scenarios (five random seeds each), the residual detector achieved an area under the receiver operating characteristic curve (AUROC) of 0.918 (standard deviation (SD) 0.081), an area under the precision–recall curve (AUPRC) of 0.622 (SD 0.372), and a Matthews correlation coefficient (MCC) of 0.573 (SD 0.232). Capture-disjoint and leave-one-capture-out evaluation reduced AUPRC to 0.553 and 0.521, respectively; simultaneous 5% contamination of adaptation and calibration data reduced it to 0.548. At α = 0.05, EBR yielded a block false-positive rate (FPR) of 0.072 and attack-block recall of 0.786, although the merged alarm burden remained 11.1 events per hour. Refitting on ROAD preserved ranking performance (AUROC 0.793; AUPRC 0.503) but produced low and variable alarm recall. These findings support target-specific workflow replication; capture separation, commissioning-data quality, and operating-point selection remain critical for deployment.

Abstract: Artificial intelligence (AI) is used throughout autonomous vehicle (AV) software, yet published comparisons of perception, estimation, prediction, planning and control methods rarely share data, protocols or statistical analysis, which makes their trade-offs difficult to judge. This paper reports a reproducible experimental evaluation that spans the AV stack under a single protocol: fixed seeds, held-out test data, validation-based tuning, 95% confidence intervals (CIs), and paired non-parametric tests with multiplicity correction. Twelve experiments address four research questions using the public comma10k driving dataset, seeded simulations, and public field data. On 150 held-out real frames, context features raise drivable-area intersection over union (IoU) from 0.701 to 0.803 (p < 10−11), whereas a classical lane detector reaches an F1 score of 0.658 [0.620, 0.694] irrespective of frame brightness (p = 0.93). Odometry–GNSS fusion reduces outage error from 265.5 m to 30.2 m. Multimodal prediction halves displacement error only because it outputs several hypotheses; its single-hypothesis variant is the worst model. Q-learning reduces lane-change crash rate from 59.8%to 6.3% relative to a rule with identical inputs, and validation-tuned controllers reverse the rankings obtained with hand-picked gains. FedAvg recovers 54–74% of the accuracy gap between isolated and centralised training. A re-analysis of 220.6 million rider-only miles of Waymo data confirms an 83%reduction in injury crashes (ratio 0.170 [0.143, 0.201]), while about 388 million miles would be needed to demonstrate an 80% fatality reduction. All code and data are released.

Abstract: Reverse parking of a vehicle with trailer is a challenging task to complete for human drivers due to its articulated structure, unstable reverse-motion behaviors and unintuitive steering responses. This paper proposes a compact, trailer-centric nonlinear model predictive control (NMPC)-based automation routine that integrates local motion generation and feedback control into a single receding-horizon framework, without requiring a separately planned reference path or a dedicated path-tracking controller. The trailer unit is represented as a virtual standalone vehicle, and its motion requirements are mapped to the vehicle unit inputs via inverse kinematics. This allows trajectory prediction and tracking objective to be represented only with trailer states, hence reducing the dimensions of the horizon-stacked state and weighting matrices compared to using the full vehicle-trailer formulation. The proposed controller is implemented as a single-shooting nonlinear program (NLP) and supports a three-staged maneuver sequence to obtain an improved final parking configuration. Simulation results demonstrate successful vehicle-trailer parking maneuvers, while hardware-in-the-loop (HIL) results further illustrate that online NMPC computation times can remain within the real-time sampling deadline, indicating that the proposed NMPC framework can offer a practical receding-horizon automation routine for vehicle-trailer reverse parking tasks.

Abstract:

Automotive perception is migrating from self-contained edge sensors toward satellite architectures in which lightly processed data is streamed to a central compute unit. That migration is already underway commercially, and cost reduction is the reason usually given for it. This paper argues that cost is the least interesting consequence of centralisation, and that the decisive one has been largely overlooked: the choice of where to partition the receive chain determines whether the vehicle’s separate radar apertures can be combined phase-coherently into a single synthetic aperture. We define a partition-point taxonomy (P0–P5) for the automotive radar receive chain and show that the two partition points which preserve a shared frequency reference — analogue IF transport (P1) and raw-ADC transport (P2) — enable an aperture whose extent is set by the vehicle, not by the sensor module. We propose an architecture in which each mounting point carries only a Remote Antenna Front-End (RAFE: array, LNA, mixer, frequency multiplier, IF conditioning — no ADC, no DSP, no MCU, no PHY), connected to a unified ADAS Integrated ECU (AIECU) by a single coaxial cable carrying DC power, an up-link frequency reference, and a frequency-division-multiplexed down-link of the IF channels. Quantitatively, for four 3Tx×4Rx panels distributed across a 0.61 m fascia at 79 GHz: the virtual aperture grows from 20.87 mm to 0.605 m, the -3 dB beamwidth narrows from 8.49◦ to 0.255◦, and the Cramér–Rao bound on azimuth improves by 36.5 dB. In single-snapshot Monte Carlo the measured azimuth RMSE at 15 dB SNR improves from 0.203◦ to 0.0028◦, a factor of 71. Two-target resolution at 90 % probability improves from 4.5◦ to 0.15◦. These gains are gated by an unforgiving synchronisation requirement that we quantify: a 0.5 dB coherent-gain budget allows an inter-node RMS carrier-phase error of 0.339 rad, equivalent to 0.68 ps of residual delay or 102 µm of panel displacement at 79 GHz. Timestamp synchronisation cannot approach this — IEEE 802.1AS gPTP at 100 ns corresponds to 4.96 × 10⁴ rad — and neither can an open-loop shared reference, because thermal drift of a 3.5 m coaxial feed at 60 ppm/K over a 145 K automotive range produces 145 ps, or 11.5 whole cycles of carrier phase. We show that closed-loop loopback delay calibration closes the gap, requiring 46 dB of calibration SNR over a 200 MHz sweep against an available link SNR of 96.7 dB. We report a negative result that constrains the design space: the per-node beam is far too broad to disambiguate the coherent aperture. The optimised sparse array’s first grating lobe sits at 1.89◦, while the node beamwidth is 10.42◦, so node-level gating provides no ambiguity protection whatsoever. Ambiguity must instead be managed by co-array-aware panel placement, which reduces the peak sidelobe from -0.20 dB to -3.20 dB and the ambiguity rate at -6 dB SNR from 64.5 % to 21.3 %. At system level, worst-case sensor-to-actuation latency falls from 32.8 ms to 19.6 ms; Monte Carlo over triangular cost distributions gives a mean BOM reduction of 31.8 % against edge sensors (90 % interval $195–$552) and 24.0 % against satellite radar; and a fault-tree analysis shows that the architecture reaches an ASIL-D PMHF of 3.5 × 10⁻⁹/h only when the AIECU is genuinely fail-operational, tolerating a common-cause factor up to β = 0.20.

Abstract: Early fault warning in lithium-ion battery packs is challenged by weak labels and changing operating conditions. This study develops an unsupervised cell-level framework that integrates Local Outlier Factor (LOF) scoring with a validity-gated Streaming Peaks-Over-Threshold (SPOT) rule. Each pack-median-centered voltage trajectory of seven-sample length is characterized by five descriptors, and the maximum cell LOF is logarithmically stabilized as a pack-level anomaly score. The online threshold is estimated via a generalized Pareto distribution (GPD) only when graphical and bootstrap goodness-of-fit diagnostics support the calibrated tail; otherwise, the decision falls back to a higher empirical quantile. Evaluation on real-world vehicles and CH-BatteryGen cases shows that aggregate false-alarm rates are 1.85% and 1.64% for pre-event baselines and normal references at a target risk parameter q = 0.01, respectively. All seven recorded field events are preceded by at least one alarm within 24 hours, yielding a median lead time of 17.39 hours. The tail-validation gate retains GPD-SPOT in 12 of the 15 streams, while the remaining three adopt the empirical fallback. In the three labeled lithium iron phosphate (LFP) cases, the supplied faulty cell ranks first. The framework reports alarm times, suspicious-cell rankings, and the selected thresholding method without fault-labeled training data.

Abstract: This paper presents a novel advanced mathematical framework for motion planning of autonomous vehicles (AVs), centered on the simultaneous optimization of spatial trajectories and kinematic profiles within an augmented Frenet coordinate system. Addressing the inherent limitations of traditional two-stage planning approaches - namely, geometric discontinuity and the control-chattering trap - we propose a «virtual rail» concept that decouples the spatial reference curve from temporal velocity distribution while maintaining high-order differentiability (up to the 3rd derivative of curvature). By employing a finite element approach based on 3rd-order Hermite polynomials with two degrees of freedom per node for constraint satisfaction, the proposed framework ensures maximum trajectory smoothness, which is critical for actuation stability. Furthermore, we derive the complex kinematic relationships (velocity, acceleration, and jerk) directly in the curvilinear reference frame and formulate an optimization objective that balances trajectory fidelity, ride comfort and dynamic feasibility. The proposed mathematical framework demonstrates high consistency and robust convergence, validating the underlying analytical approach as a sound and rigorous basis for complex motion planning. This work establishes the foundational architecture for a series of studies focusing on higher-order vehicle dynamics and multi-segment transient maneuver planning.

Abstract: Traction electric machines in electrified mobility are constrained by limited supply voltage and critical materials. Active winding reconfiguration (AWR) changes the winding connection during operation to improve voltage utilization and field-weakening capability. However, general design rules for AWR remain limited. This work extends the characteristic-current-based normalized parameter-plane framework to AWR via topology-dependent reconfiguration factors and evaluates the resulting torque-speed-power behavior over the design space. The analytical model is compared against finite-element analysis of six baseline machines and test-bench measurements from one machine. Normalized characteristic current is the dominant design parameter, capturing the balance between magnetic and current loading, while saliency and the reconfiguration factor set the magnitude of the benefit. Figures of merit are defined to quantify reconfiguration benefit across peak performance, crossover points, torque-speed envelopes, and part-load performance. Across these figures of merit, three explicit thresholds for normalized characteristic current are identified. Below the lowest threshold, designs such as synchronous reluctance and ferrite-assisted machines gain of up to four times higher constant-power over speed range (CPSR) and 40% higher peak power. Above it and up to the middle threshold, conventional rare-earth traction machines gain mainly in part-load efficiency instead. Above the highest threshold, narrow-field-weakening designs gain mainly in speed-range extension. These thresholds provide generalized, geometry-independent early-stage AWR screening for traction machine concepts.

Abstract: Gearbox noise, vibration, and harshness (NVH) simulation has progressed substantially beyond nominal transmission-error prediction. Contemporary approaches can incorporate manufacturing and assembly tolerances, measured tooth geometry, flexible gear-shaft-bearing-housing interactions, variable-speed operation, acoustic radiation, surrogate modeling, psychoacoustic metrics, and physical-virtual model updating. In parallel, industrial gear production is moving toward near-100% tooth inspection, order-based waviness analysis, process monitoring, virtual end-of-line (EOL) assessment, and manufacturing feedback. Consequently, many capabilities frequently described as future directions already exist individually. This critical review therefore addresses a different question: how strongly are increasingly integrated production-NVH architectures supported by physical evidence? A targeted evidence set of 46 external scientific, doctoral, industrial, and patent sources was classified using a two-dimensional Architecture x Evidence framework. Architectural integration ranges from nominal simulation to unit-specific NVH digital twins and closed-loop manufacturing, whereas evidence maturity ranges independently from conceptual disclosure to component validation, same-unit production validation, population-level validation, and intervention validation. Thirteen of the 46 coded sources reached highly integrated architecture levels A4-A5, yet none reached the E3-E5 range under the adopted criteria. Four traceability requirements are identified for credible production-oriented NVH digital twins: part, physical, spectral, and statistical traceability. Major open problems include propagation of measured joint production distributions, separation of excitation and transfer-path variability, NVH-preserving reduction of high-density tooth metrology, physics-preserving order-map surrogates, quality-control validation of virtual EOL systems, and controlled demonstration that model-selected manufacturing interventions reduce measured acoustic risk. The next generation of gearbox NVH simulation is therefore expected to be defined less by increasing the numerical complexity of an ideal virtual gearbox and more by strengthening the evidence connecting virtual predictions with real manufactured units and production populations.

Abstract: Manufacturing deviations in geared drivetrains alter tooth contact, transmission error (TE), mesh stiffness, bearing-transmitted forces, structural vibration and radiated noise, yet these effects are often studied in disconnected models. This methodological review develops a version-verified manufacturing-aware NVH workflow around Romax DT, with Romax Spectrum as the central system-dynamics environment. Official Romax Help and release documentation are used to establish capability and software-version boundaries, while peer-reviewed literature is used to assess physical evidence and validation requirements. A structured literature search and capability audit show that prior studies demonstrate individual links—measured geometry to LTCA/TE, Romax-based system vibration, acoustic coupling, or EOL correlation—but not the complete, version-audited chain. The proposed workflow connects measured cylindrical-gear flank data through GDE, loaded tooth contact analysis, 2025.1 advanced three-dimensional finite-element-based tooth stiffness in Gearbox Transmission Error (GBTE), flexible system response, Equivalent Radiated Power (ERP), detailed acoustic analysis and external experimental validation. It explicitly separates vendor-documented capability, peer-reviewed evidence and proposed external extensions such as wear-state updating, model calibration and EOL data assimilation. A falsifiable validation hierarchy is defined for comparing nominal, tooth-averaged and tooth-resolved measured states at contact/TE, housing-vibration and acoustic levels. The contribution is therefore methodological and reproducibility-oriented rather than new solver physics.

Abstract: The increasing connectivity of intelligent vehicles exposes in-vehicle networks to message tampering, replay, impersonation, and unauthorized access. Existing secure communication schemes commonly rely on pre-shared keys or certificate-based public-key infrastructures, which introduce considerable key-distribution and certificate-management overhead when a large number of electronic control units (ECUs) are deployed. To address these issues, this paper proposes a lightweight identity-based secure communication scheme for in-vehicle networks. The proposed scheme separates low-frequency authenticated session establishment from high-frequency data protection. Identity-bound private keys are provisioned offline by a trusted key generation center, and the T-Box and target ECU establish a shared session key through mutual authentication and explicit key confirmation. Direction- and purpose-specific subkeys are subsequently derived to protect bidirectional communication and provide confidentiality, integrity, source authentication, and replay protection. Formal analysis verifies the security properties of the proposed scheme. Experimental results show that the median single-ECU handshake latency is 287.560 ms, while the data-plane processing latency is 1.036 ms per 8-byte message. Under a 500-kbit/s classical-CAN model, the proposed configuration requires 138.75 bits per message and produces a modeled bus load of 49.95% for 18 ECUs. Moreover, all 1200 frame-mutation trials were rejected without unauthorized plaintext release. These results indicate that the proposed scheme provides a practical approach to lightweight and scalable secure communication in in-vehicle networks.

Abstract: The added battery mass of electric vehicles intensifies the comfort–road-holding conflict of passive suspensions, while experimentally validated magnetorheological (MR) damper models for production electric vehicles remain scarce. This study characterizes an MR damper designed as a direct replacement for the front strut of a compact electric SUV and validates its quasi-steady simulation model. A single-degree-of-freedom slider–crank test rig was developed in which the piston velocity is reconstructed kinematically from the known crank geometry, thereby avoiding numerical differentiation of a measured displacement signal. A two-stage experimental campaign was conducted: a Taguchi L9 screening identified the control current as the most influential of the three tested factors, and a subsequent 25-point full-factorial matrix spanning peak piston velocities of 0.1–0.3 m/s and coil currents of 0–2.0 A provided the validation data set. A modified Bingham model—with the yield force calibrated by finite-element magnetostatic analysis and the viscous coefficient identified from the 0 A baseline—reproduced the measured force–velocity characteristics with a mean deviation of 10.7% at 0.5 A and 18.6% in deep magnetic saturation; the physical mechanisms responsible for the residual deviation are identified and model refinements are proposed; the validated model is intended as a plant-level foundation for the semi-active and data-driven suspension controllers required by electrified vehicle platforms.

Abstract: Articulated steering systems are pivotal in off-road vehicles operating in constrained and narrow spaces. This research explores the mathematical modelling of the Stepper Motor Driven Orbitrol Valve (SMDOV), focusing on steady-state performance and validating findings through experimental data. Using the MATLAB®/Simulink platform, a comprehensive mathematical model of the system is developed. Experimentally, a sprocket-chain drive mechanism links the stepper motor to the orbitrol valve, which can provide a fixed steering rate. Further, the effects of valve leakage, flow responses under varying steering rates, and external loads are analysed. The model validation offers insights into the influence of damping coefficients and leakage resistance on the system performance. Moreover, an empirical relationship is proposed based on system inputs for future investigations into the dynamic behaviour of the Orbitrol valve-driven articulated steering system. This work highlights key parametric values of the SMDOV system and advances the automation of articulated vehicle steering mechanisms. This article provides more significant insights about the stepper motor driven orbitrol valve characteristics. It helps in developing the orbitrol valve controlled automatic steering mechanism for better control of the articulated vehicles.

Abstract: To comprehensively improve vehicle ride comfort and handling stability, as well as to enhance the robustness and disturbance rejection capability of the suspension system under uncertain operating conditions, this paper first establishes a full vehicle suspension dynamic model. On this basis, an improved SH-ADD controlled suspension is proposed as a reference model through the analysis of power flow characteristics within the suspension system, and a sliding mode control (SMC) strategy is subsequently designed based on this reference model. Simulation results indicate that, compared with conventional control strategies, the proposed algorithm offers a clear advantage in reducing the root mean square (RMS) values of sprung mass acceleration, thereby effectively suppressing body vibration and improving ride comfort. In addition, both roll and pitch angular accelerations are notably reduced, contributing to enhanced body attitude stability. Moreover, when subjected to parameter uncertainties such as variations in vehicle speed and sprung mass, the proposed controller maintains excellent robustness and stable control performance, demonstrating its overall effectiveness in improving suspension system performance under complex operating conditions.

Abstract: Modern vehicles carry front and rear radar, forward and surround-view cameras, and increasingly infrared sensing as standard driver-assistance equipment. This paper investigates whether that same sensor suite, fused and fed forward into the suspension control loop, can measurably improve ride quality on unpaved and off-road surfaces, for both the vehicle chassis and, as a secondary actuation layer, the occupant seat. We formalize a sensor-fusion, road/suspension estimation, control, and feedback architecture into a state-space model; derive the governing equations for a three-degree-of-freedom quarter-car-plus-seat plant; specify a multi-sensor preview measurement model and an inverse-variance fusion law; design and compare passive, reactive, and predictive controllers; and implement and evaluate the full pipeline in Python on a synthetic ISO 8608 [2] off-road profile with embedded speed-bump test events. The predictive controller reduces ISO 2631-weighted [1] body (chassis) RMS acceleration by 23.7% and seat/occupant RMS acceleration by 67.9% relative to a passive baseline. We further replace two hand-engineered blocks with learned counterparts — a neural network that corrects context-dependent sensor bias, and a Random Forest terrain classifier that drives gain scheduling — and report the resulting, honestly modest, additional gains. A prior-art survey spanning patents [4–7,10,11] and production systems [8,9], and a review of public datasets [12–14,18] suitable for validating this class of system, precede an explicit novelty and freedom-to-operate discussion.

Abstract: Risk-based prioritization frameworks such as the Intelligent Pedestrian Model (IPM) rank pedestrians by an instantaneous, reference-normalized risk score. They indicate which pedestrian requires attention first. This study examines the temporal stability of such rankings. We computed an observation-only kinematic Exposure proxy frame by frame on three ETH/UCY benchmark scenes. In these scenes, the highest-priority identity changes rapidly: the median top-1 persistence is two frames (0.8 s). We introduce a switch classification that separates established switches from entry-driven and forced switches. Grace-period exclusion is evaluated as a sensitivity variant and shown to remove up to 82% of evaluable time in short-track scenes. Established flicker rates range from one switch per 2.7 s in dense scenes to one per 25.4 s in sparse scenes, with switches concentrated at small top-1–top-2 risk gaps. The results show that instantaneous rankings alone may be insufficient for sustained attention allocation and motivate future work on temporal priority management.

Abstract: Adaptive cruise control (ACC) and related advanced driver assistance systems (ADAS) regulate vehicle speed mainly for safety and comfort. In a battery electric vehicle (BEV), the same speed decisions also determine energy use, and therefore range. This concept paper couples the two concerns: a predictive longitudinal controller is developed, from first principles and without empirical validation, that plans ADAS speed and manages BEV energy within a single problem. The vehicle longitudinal dynamics, the powertrain efficiency map, and the regenerative brake-blending limits are embedded in a model predictive control (MPC) formulation whose cost function weighs gap-keeping safety, ride comfort, and battery energy over a receding horizon, informed by electronic-horizon (map and traffic) preview. To make the controller deployable, the safety-critical deceleration path is separated from the energy-optimization layer; this separation is supported by a worked hazard analysis (HARA) and by the derived ISO 26262 safety goals. A first-principles calculation shows that keeping a deceleration within the regeneration power limit recovers substantially more energy than late, hard braking. No simulation or vehicle data are reported: the contributions are the formulation, the safety architecture, and a concrete evaluation protocol, with its main threats to validity, to guide the quantitative study that should follow.

Abstract: Public run-to-failure datasets support transparent condition monitoring, but the sensitivity of simple vibration features to analysis choices is rarely reported. This study presents a reproducible raw-signal-to-result workflow for 20 MATLAB files from the public University of New South Wales spur-gear wear dataset. Feature sensitivity is evaluated for spectral window, record length, Welch segment length, sideband order, local harmonic-band width, and small speed-reference biases; a Hilbert-envelope spectrum provides a selective demodulation baseline. In the dry sequence, both vibration-channel root-mean-square values increased more than threefold, and the second gear-mesh harmonic increased by a factor of 3.53. In the lubricated sequence, global RMS changed weakly, whereas the 2–13 kHz and 20–37 kHz band-RMS measures and first-harmonic sideband index had Spearman correlations of 0.782, 0.758, and 0.830. Broad-band RMS trends remained positive across all tested windows, record lengths, and Welch settings. Exact-bin harmonics were sensitive to speed-reference bias, whereas a ±5 Hz local-band RMS retained the dry second-harmonic correlation of 0.842 over ±0.1% bias. The lubricated 2–13 kHz envelope first-order index was strongly negative (ρ = −0.782), indicating complementary rather than consistently superior behavior. The contribution is a reproducible parameter-robustness map and practical guidance for selecting interpretable first-level gear-wear indicators.

Abstract: Pedestrian-detection research and vehicle-safety assessment have traditionally concentrated on upright, walking or crossing pedestrians. People who are prone, supine, lateral, seated, crouched, kneeling, partially collapsed or undergoing a fall present different visual, geometric, thermal and kinematic characteristics. These differences can reduce transferability from conventional benchmarks and make apparently similar studies difficult to compare when posture definitions, data provenance, environmental conditions, latency accounting and safety assumptions are reported inconsistently. This article proposes NUP-REPORT 1.0, a minimum reporting and benchmarking framework for non-upright pedestrian detection and pre-crash safety evaluation. The framework was developed through a structured narrative synthesis of five evidence streams: epidemiology of pedestrians lying on the road; pedestrian and multispectral detection benchmarks; uncertainty and calibration methods; scenario-based automated-driving evaluation; and current public safety standards and assessment protocols. A failure-chain decomposition was then used to define six reporting domains: target and posture; scenario and environment; sensors and data provenance; model and fusion architecture; performance and uncertainty; and vehicle-level safety interpretation. NUP-REPORT further proposes a minimum scenario matrix, a core outcome set, explicit timing definitions, stopping-margin equations and a 30-item checklist. The framework distinguishes object-detection accuracy from safety-relevant performance by requiring posture-stratified outcomes, time-to-first-detection, end-to-end latency, confidence calibration, sensor-degradation sensitivity, safety-critical false negatives and transparent vehicle-response assumptions. NUP-REPORT is not a regulatory test protocol, consensus standard or performance threshold. It is a versioned reporting proposal intended to improve interpretability, reproducibility and comparability while supporting future dataset development, inter-laboratory validation and standards engagement.

Abstract: This simulation-based case study investigates vehicle handling performance during severe asymmetric and symmetric tire inflation pressure loss, comparing self-supporting run-flat tires (SSRFT) to conventional radial configurations. To capture the complex coupled interactions occurring under severe pressure depletion, this work introduces a non-linear 9-Degree-of-Freedom (9-DOF) multi-body dynamics numerical simulation framework parameterized to an F56 MINI Cooper S platform. The mathematical plant model expands upon classical planar approximations by fully integrating dynamic chassis roll, pitch, and four independent wheel spin rotational degrees of freedom. The core tire-road interface is parameterized using empirical constants parsed from a proprietary Michelin Magic Formula 6.2 (.tir) baseline data file acquired through manufacturer collaboration. Because physical test-rig boundaries prohibit zero-pressure execution, unpressurized (0 kPa) carcass-only structural degradation profiles and sidewall stiffness retention indices are formulated based on hyperelastic limits from literature. To eliminate human driver variability and bypass the limitations of open-loop steering inputs, path tracking is governed by a closed-loop, two-point preview Stanley steering control algorithm. The vehicle model is subjected to the strict spatial corridor constraints of an unthrottled ISO 3888-2 double-lane change maneuver at an entry speed of 80 km/h under an unthrottled inertial speed decay regime. To map the true limits of structural failures, three distinct puncture topologies are evaluated sequentially: asymmetric Front-Left (FL) deflation, asymmetric Front-Right (FR) deflation, and symmetric dual front-axle deflation. A complete parametric sensitivity analysis is performed on the primary tracking gains to isolate structural tire behavior from the guidance loop. Quantitative model-based observations demonstrate that while conventional radial plies enter rapid understeer saturation and continuous contact patch sliding, the structural sidewall insert rubber (SIR) of the SSRFT maintains stable handling margins and minimizes lateral track errors. These quantified performance variations provide baseline metrics to inform virtual vehicle prototyping and parametric chassis control tuning.