Browse Topic: Sensors and actuators
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
As automotive technology advances, the need for comprehensive environmental awareness becomes increasingly critical for vehicle safety and efficiency. This study introduces a novel integrated wind, weather, and motion sensor designed for moving objects, with a focus on automotive applications. The sensor’s potential to enhance vehicle performance by providing real-time data on local atmospheric conditions is investigated. The research employs a combination of sensor design, vehicle integration, and field-testing methodologies. Findings prove the sensor’s capability to accurately capture dynamic environmental parameters, including wind speed and direction, temperature, and humidity. The integration of this sensor system shows promise in improving vehicle stability, optimizing fuel efficiency through adaptive aerodynamics, and enhancing the performance of autonomous driving systems. Furthermore, the study explores the potential of this technology in contributing to connected vehicle
Door sunshade in a vehicle has proven to be very comfortable and luxurious feature to the customers. Luxury vehicles provide power sunshade which is electrically operated with the activation of a switch, whereas cost conscious vehicles provide manual sunshade which requires manual coiling and uncoiling. This study is to develop a door panel structure that can accommodate both the manual sunshade and power sunshade, thereby serving both cost conscious as well as luxury seeking customers. Manual sunshade consists only of cassette, pull bar, spindle mechanism and hooks whereas the power sunshade consists of cassette, pull bar, spindle mechanism, flap mechanism, bowden cable mechanism, actuator and motor. Due to this difference in package, it becomes difficult to accommodate both variants of sunshade into the same body system. However, this study helps in developing a common body structure by ways of effective packaging, modifying the cable and actuator mechanism and critical packaging of
Automotive seating systems have become increasingly sophisticated, providing consumers with more flexible configurations and comfort functionalities. Traditional power seating, which relied on a few motors to adjust the seat position, has evolved into more technically advanced reconfigurable systems equipped with additional feedback sensors and actuators. These advancements include features such as Easy Entry, Zero Gravity, Stadium Swivel, IP Nesting, Auto Lumbar/Bolster Adjustment and Power Long Rails. All the features indicate that the overall control of seating systems now resembles robotic arm control or multi-body control, involving numerous coordinated movements. In this paper, we propose a novel control strategy for the coordinated speed control of multiple motors. Unlike traditional seating controls, which typically use direct switches or open-loop systems, we introduce a feedback approach that incorporates Kalman-filter-based speed estimation using raw signals directly from
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
As automotive technology advances, modern vehicles increasingly rely on complex electronics such as cameras, sensors, radar and lidar. These components are critical for advanced driver-assistance systems (ADAS) and automated driving. With the growing complexity of these systems, automotive manufacturers face challenges in efficiently transmitting both power and data while minimizing weight and system complexity. Power over Coaxial (PoC) technology offers a solution by allowing the transmission of power and data over a single coaxial cable, significantly simplifying vehicle design. With the integration of more electronic systems, especially those required for ADAS and autonomous driving, the demand for power and high-speed data transmission in vehicles has surged. Modern cars now use multiple cameras and sensors, and as vehicle systems continue to evolve, the number of electronic components is expected to increase. This shift places significant demands on the transmission of both data
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn't leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
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