Browse Topic: Vehicle drivers
This SAE Recommended Practice is intended to establish a procedure to certify the fundamental 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. The associated family of documents listed below establish driving skill criteria for various specific categories. SAE J3300: Driving level SAE J3300/1: Low mu/winter driving SAE J3300/2: Trailer towing SAE J3300/3: Automated driving Additional certifications to be added as appropriate. This main document provides: (1) common definitions and general guidance for using this family of documents, (2) directions for obtaining certification through Probitas Authentication®1, and (3) driving level examination requirements.
Sound power is a commonly used metric to quantify acoustic sources like AC motor in electrified powertrain. Testing for sound power determination is often performed in an anechoic environment to create free-field conditions around the unit. To eliminate the influence of extraneous noise sources, the anechoic facilities must be further isolated from driver and absorber dynamometers. These dynamometers are needed for running the AC motors in the desired speed and load conditions. For early detection of potential issues, it is advantageous to have the capability for engineers to conduct acoustic tests in standard laboratory environments. These may include non-acoustically treated rooms, presence of extraneous noise sources (e.g., driver and absorber dynos), etc. In such environments, sound intensity-based sound power determination methods could be utilized. The sound intensity-based approach is covered in ISO 9614 standard. The norm is to sweep an intensity probe on a sound source in
In addition to providing safety advantages, sound and vibration are being utilized to enhance the driver experience in Battery Electric Vehicles (BEVs). There's growing interest and investment in using both interior and exterior sounds for pedestrian safety, driver awareness, and unique brand recognition. Several automakers are also using audio to simulate virtual gear shifting of automatic and manual transmissions in BEVs. According to several automotive industry articles and market research, the audio enhancements alone, without the vibration that drivers are accustomed to when operating combustion engine vehicles, are not sufficient to meet the engagement, excitement, and emotion that driving enthusiasts expect. In this paper, we introduce the use of new automotive, high-force, compact, light-weight circular force generators for providing the vibration element that is lacking in BEVs. The technology was developed originally for vibration reduction/control in aerospace applications
The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results
As the automotive industry moves toward electrification, new challenges emerge in keeping pleasant acoustics inside vehicles and their surroundings. This paper proposes a method for anticipating the main sound sources at driver’s ear for custom driving scenarios. Different categories of Road and Wind noise were created from a dataset of multiple vehicles. Using innovative sound synthesis techniques, it enables Valeo to make early predictions of the emergence of an electric axle powertrain (ePWT) once it is combined with this masking noise. Realistic signals could be generated and compared with actual acoustic measurements to validate the method.
This practice presents methods for establishing the driver workspace. Methods are presented for: Establishing accelerator reference points, including the equation for calculating the shoe plane angle Locating the SgRP as a function of seat height (H30) Establishing seat track dimensions using the seating accommodation model Establishing a steering wheel position Application of this document is limited to Class-A Vehicles (Passenger Cars, Multipurpose Passenger Vehicles, and Light Trucks) as defined in SAE J1100.
These general operator precautions apply to off-road work machines as defined in SAE J1116. These should not be considered as all-inclusive for all specific uses and unique features of each particular machine. Other more specific operator precautions not mentioned herein should be covered by users of this recommended practice for each particular machine application.
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
This paper explores the application of a modeled torque converter in the real-time control of a hybrid electric powertrain. The study aims to determine the optimal gear selection and engine speed target required to meet driver demands. It also delves into the concept of torque converter input inertia compensation, particularly during open, open-to-close, and close-to-open states. The primary objective is to achieve the intended driver torque while minimizing torque sag and bumps during these transitions. This approach ensures improved powertrain response and maintains system integrity within the operational limits of the battery, motors, and engine.
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the
This paper introduces a novel approach to optimize battery power usage and optimal engine torque for Axle disconnect device engagement under power constrained scenarios for range extended hybrid vehicles. Range extended hybrid architecture provides benefits of BEV architecture and relief the range anxiety that BEV drivers often have. The Axle disconnect device helps improve the efficiency of the battery power usage when it is disconnected and provides better drivability and performance to fulfill driver demand when it is connected [1]. Under power constraint scenario, the disconnect device engagement could take too long or eventually fail to engage and result in degradation for drivability and vehicle level performance. This novel approach is utilizing the engine to either generate more power to spin up the disconnect motor faster under discharge limited case or generate less power to allow the disconnect motor to spin down under charge limited case. The effectiveness of this approach
The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to
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