Search Results (4)

With the development of advanced driver assistance systems (ADAS) and autonomous vehicles (AV), recent years have seen an increasing evolution of onboard sensors and communication interfaces capable of interacting with available infrastructures, including satellite constellations, road structures, modern and heterogeneous network systems (e.g., 5G and beyond) and even adjacent vehicles. Consequently, it is essential to develop architectures that cover data fusion (multi–sensor approach), communication, power management, and system monitoring to ensure accurate and reliable perception in several navigation scenarios. Motivated by the EMERGE project, this paper describes the definition and implementation of an On Board Unit (OBU) dedicated to the navigation process. The OBU is equipped with the Xsens MTi–630 AHRS inertial sensor, a multi–constellation/multi–frequency Global Navigation Satellite System (GNSS) receiver with the u–blox ZED–F9P module and communication interfaces that afford access to the PointPerfect augmentation service. Experimental results show that GNSS, with corrections provided by augmentation, affords centimetre accuracy, with a Time To First Fix (TTFF) of about 30 s. During the on–road tests, we also collect: the output of fusion with inertial sensor data, monitoring information that assess correct operation of the module, and the OBU power consumption, that remains under 5 W even in high–power operating mode. Full article

The present paper focuses on vehicle simulator fidelity, particularly the effect of motion cues intensity on driver performance. The 6-DOF motion platform was used in the experiment; however, we mainly focused on one characteristic of driving behavior. The braking performance of 24 participants in a car simulator was recorded and analyzed. The experiment scenario was composed of acceleration to 120 km/h followed by smooth deceleration to a stop line with prior warning signs at distances of 240, 160, and 80 m to the finish line. To assess the effect of the motion cues, each driver performed the run three times with different motion platform settings–no motion, moderate motion, and maximal possible response and range. The results from the driving simulator were compared with data acquired in an equivalent driving scenario performed in real conditions on a polygon track and taken as reference data. The driving simulator and real car accelerations were recorded using the Xsens MTi-G sensor. The outcomes confirmed the hypothesis that driving with a higher level of motion cues in the driving simulator brought more natural braking behavior of the experimental drivers, better correlated with the real car driving test data, although exceptions were found. Full article

Foot-mounted inertial pedestrian positioning (FIPP) plays an important role for facilitating pedestrian activities. It is suitable for indoor environment applications where global navigation satellite systems are unavailable such as during firefighting and military actions. However, the positioning error of FIPP can increase rapidly due to the measurement noise of the sensors. Zero Velocity Update (ZUPT) is an error correction method proposed to solve this accumulative error. However, the heading misalignment angle, which results in a continuous increase in the positioning error, cannot be estimated by ZUPT. In order to solve this problem, the improved ZUPT based on the Improved Attitude Algorithm (IAA) according to accelerometer measurements is proposed in this paper. When a pedestrian is in the stance phase, the horizontal attitude is estimated by using accelerometer measurements. According to the relationship between the heading misalignment angle and horizontal attitude, the heading misalignment angle is obtained by a series of mathematical derivations. By taking the velocity error and the attitude misalignment angle as observations, the heading misalignment angle and positioning error can be estimated and compensated for through the Kalman filter. Finally, we use MTI-G710 sensor manufactured by XSENS for the actual test and the experiment results show that the proposed method is effectively correct. Full article

Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of the relatively low measurement accuracy of the measurement sensor. The zero-velocity update (ZUPT) is an error correction method which was proposed to solve the cumulative error because, on a regular basis, the foot is stationary during the ordinary gait; this is intended to reduce the position error growth of the system. However, the traditional ZUPT has poor performance because the time of foot touchdown is short when the pedestrians move faster, which decreases the positioning accuracy. Considering these problems, a forward and reverse calculation method based on the adaptive zero-velocity interval adjustment for the foot-mounted MIMU location method is proposed in this paper. To solve the inaccuracy of the zero-velocity interval detector during fast pedestrian movement where the contact time of the foot on the ground is short, an adaptive zero-velocity interval detection algorithm based on fuzzy logic reasoning is presented in this paper. In addition, to improve the effectiveness of the ZUPT algorithm, forward and reverse multiple solutions are presented. Finally, with the basic principles and derivation process of this method, the MTi-G710 produced by the XSENS company is used to complete the test. The experimental results verify the correctness and applicability of the proposed method. Full article