Browse Topic: Vehicle drivers
This SAE Recommended Practice is intended to establish a procedure to certify the low mu/winter driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. This document provides directions for obtaining certification through Probitas Authentication®1 and the low mu/winter driving skill examination requirements. This document is a supplement to SAE J3300, providing information specific to the low mu/winter driving skill certification and clarifying the application of the rules set forth in SAE J3300 to the low mu/winter driving certification. While the references, definitions, rules, and guidelines presented in SAE J3300, Sections 1 through 5 apply to the low mu/winter driving certification, they are not repeated in this document.
This study compares two international loudness standards—International Organization for Standardization (ISO) 532-1:2017 (Zwicker) and ISO 532-3:2023 (Moore–Glasberg–Schlittenlacher)—for assessing vehicle wind noise using wind tunnel measurement data. The two methods demonstrate good agreement in ranking wind noise quietness at typical highway speeds (120–140 km/h). However, discrepancies arise under specific conditions due to ISO 532-3’s binaural processing, which accounts for interaural inhibition effects. These differences are particularly pronounced in vehicles with asymmetric wind noise levels between the driver and passenger sides. Power-law regression analysis reveals that binaural loudness at the driver side (ISO 532-3) increases at a slower rate with wind speed compared to the monaural measurements [driver outboard ear (DOE)] of ISO 532-1. Under yaw angle conditions, the perceptual contribution of the driver inboard ear (DIE) introduces subtle metric-dependent variations. Contribution analyses further highlight method-specific sensitivities to noise source location and direction. Overall, this study provides a valuable reference for implementing loudness metrics in automotive wind noise engineering.
To address the lack of safe and effective on-site vehicle blocking and control methods in the event of fires or other emergencies in extra-long tunnels — which can significantly reduce traffic safety risks and prevent secondary accidents — this study proposes a novel barrier-free light–smoke curtain interception method. The method integrates conventional traffic safety warning facilities (gantry-mounted variable message signs and audio–visual alarms) with two light–smoke curtain interception images to form a composite early-warning and interception system. Driving simulation experiments were conducted to comprehensively evaluate its warning effectiveness, interception performance, and operational safety in comparison with methods employing only traditional warning facilities or light curtain images. Furthermore, field drills were performed to validate its real-world applicability and interception effectiveness under both daytime and nighttime conditions. The main findings are as follows: 1) The fixation ratio and interception success rate associated with the proposed method were significantly higher than those of the other two methods, demonstrating enhanced visual attention and superior warning and interception performance. 2) The maximum deceleration observed with the proposed method was lower than that of the light curtain–only method and did not trigger emergency braking, thereby indicating high operational stability and driver comfort. 3) In field drills, after activation of the interception equipment, only one and two vehicles entered the tunnel under daytime and nighttime conditions, respectively, and full control of on-site vehicles was achieved within two minutes without any traffic accidents, verifying the system’s rapid response and effective safety assurance.
To accurately assess the navigation safety status of LNG vessels in port waters and balance safety control with waterway capacity efficiency, this study constructs a 3D dynamic safety domain model for port LNG vessels, integrating human–ship–environment multi-factors. The model introduces the Weibull function to quantify the impact of drivers’ knowledge, skills, and physiological-psychological states on safety boundaries, combines a ship motion mathematical model to establish a 2D safety domain boundary equation, and incorporates hull subsidence to build a vertical dimension, forming a complete 3D model. Longitudinally, the safety distance is calculated using the car-following braking theory, while laterally, boundaries are determined by controlling the ratio of inter-vessel interference force to navigation resistance. Through static scenario analysis and dynamic simulation verification, results show that the safety domain scale is dominated by ship speed and environmental conditions, and its shape tends to shrink as the driver’s state improves, making it more suitable for actual port scenarios than traditional models. Verified with a specific LNG hub port as a case, the safety distance calculated by the model is significantly reduced compared with current specifications, while the delay impact rate and average delay time on other vessels are decreased. The research results establish a quantifiable framework for dynamic safety assessment, providing maritime administrations and on-board pilots with a scientifically-grounded tool to determine real-time safe navigation boundaries in complex port environments, balancing safety control with operational efficiency.
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
In vehicles with electrified powertrains, high-frequency tonal noise components have become increasingly prominent and can be perceived as particularly annoying by the driver. While recent advancements in international standardization — such as ECMA-74 [1] and ECMA-418 [2] — have led to powerful new algorithms for tonal noise visualization and analysis, including Tonality-Heatmaps, the measurement side still lacks sensor setups that adequately reflect the spatial sensitivity of noise, especially for tonal components. This challenge is amplified in enclosed vehicle cabins, where room modes create local minima and maxima that become increasingly dense at higher frequencies. As a result, even small head movements can lead to noticeable differences in perceived tonal noise. Current measurement approaches do not sufficiently account for this spatial variability. This contribution addresses the absence of tailored solutions for the driver’s position by introducing an improved microphone arrangement that significantly reduces the uncertainty of measured noise levels. The proposed setup considers spatial variability without compromising comfort or crash safety requirements. By enhancing the precision of tonal noise quantification, this approach provides noise-vibration-harshness (NVH) engineers with a valuable complement to modern software-based tonal analysis methods. The paper discusses the technical implementation constraints and demonstrates the comparability of the new measurement technique with conventional setups.
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
Letter from the Guest Editor
Full state feedback offers theoretically guaranteed multi-axis stability, making it superior to conventional PID controllers. There is however one drawback, a full state controller has a mathematical difficulty if the B matrix is not square and thus not invertible. This is the case for helicopters with 6 degrees of freedom and 4 inceptors. Variations of linear quadratic regulators are a work around, however complexity dramatically increases. Best would be a direct solution to the original problem. This is the breakthrough result of this paper. This paper documents an approach which removes the analysis roadblock by partitioning the 6 x 6 system "A" matrix into two groups of 4 x 4 matrices. The 4x4 matrices are individually stabilized with full state gain matrices. One matrix is designated “Driver Matrix” which provides actuator commands. The other matrix is designated "Reference Matrix" which provides references. The two matrices are coupled together by requiring that the driver matrix follow references generated by the reference matrix. With each matrix individually stabilized, the coupled combination is also stabilized. Computation of flight dynamics states (u, v, w, p, q, r) is shared between the matrices. Initial results are very encouraging, showing an originally sluggish, heavy lift helicopter having now concise decoupled responses to pitch and roll commands. Stability derivatives are recomputed during flight allowing coverage over the whole flight envelope. A handling qualities task has been defined to relocate a 40 ft standard seaborne container directed by a pilot in a ground control station. Cooper Harper ratings of this task have demonstrated favorable Level 1 handling qualities if use is made of an automated lateral repositioning command.
This paper describes the characteristics of the Leonardo Advanced Tiltrotor Aircraft (ATA) concept, focusing on the relationship between goals, targeted improvements and enabling design features. The paper shows the design drivers such as performance, operational capabilities, and maneuverability and it describes how the attributes of the concept originated, showing trade-off and compromises approached during the genesis of the concept. The design drivers are translated into areas of interests, including download, drag, aerodynamic efficiency, rolling and yawing inertia, detectability, maintainability and engine retrofit ability. Finally, these areas are linked to the physical features of the concept, showing how they have been selected and combined to achieve the best overall benefit at platform level.
Pilots, drivers, and automated safety systems in cars and airplanes could be alerted to icy hazards by a pair of sensors developed at the University of Michigan. Ice on roads causes around 20 percent of weather-related car crashes each year, and ice build-up on planes causes roughly 10 percent of all fatal air carrier crashes by interfering with aerodynamics and controls. For instance, a flight from Brazilian airliner Voepass Linhas Aéreas crashed near São Paulo on August 9, 2024, after the plane’s de-icing systems failed, according to a report in the Aviation Safety Network. An Air France flight also crashed in the Atlantic Ocean on June 1, 2009, after ice blocked the probes that measure the plane’s speed. In both cases, all occupants died.
In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes’ intersections, and it shows improved route performance in vehicle testing. The proposed model is validated in a Comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test results show the proposed algorithm shows less path planning error than the stock openpilot end to end model when evaluated against the human driver. This proposed path planning model can be adapted to different type of vehicles for the point to point navigation task.
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