Nuksit Noomwongs

Driver Behavior Detection Based on Multi-GNSS Precise Point Positioning Technology

Driver behavior is the key to safe mobility. In general, vehicle maneuvers can be determined from acceleration of the vehicle. Physically, the acceleration and brake can be detected with longitudinal acceleration while turning and lane change can be detected with lateral acceleration. Nowadays, navigation system technologies have been much improved both on availability and accuracy with combination of multiple navigation satellite systems. Normally, it’s called Multi-GNSS (multiple global navigation satellite system). With decimeter precision and the update rate scale up to 10-Hz, the GNSS could be an alternative solution for driver behavior detection. In this paper, advance Multi-GNSS with precise point position (PPP) technique was presented with a simple maneuver detection algorithm. The advantage of PPP over conventional navigation is decimeter accuracy without direct connection to any reference base station. The experimental Multi-GNSS receiver was JAVAD Delta G3T that installed on a utility vehicle. This high performance multi-GNSS navigation system was investigated with the driving behavior detection algorithm. The precise point positioning (PPP) technique in combination with multiple satellite navigation system (GPS+GLONASS+GALOLEO+QZSS) were applied in this study. The PPP technique improved the output of detection algorithm in acceleration limit from 260% error in conventional navigation system (GPS) to 20% of incidents with PPP.

Driver Behavior Detection based On PPP-GNSS Technology

Driver behavior is one of the most important factors in safe mobility. In general, various driver maneuvers can be determined from acceleration of the vehicle. Physically, the acceleration and brake can be detected with longitudinal acceleration while turning and lane change can be detected with lateral acceleration. Normally, IMU (inertia measurement unit) has been designated to get these data. However, the IMU is not convenience to install in the vehicles especially as aftermarket parts. Nowadays, navigation system technologies have been much improved, both on availability and accuracy with combination of multiple navigation satellite systems. Normally, its called Multi-GNSS (multiple global navigation satellite system). In particular, the satellite navigation systems available in this work are GPS, GLONASS, and QZSS. With decimeter precision and the update rate scale up to 10-Hz, the GNSS can be a viable alternative for driver behavior detection. In previous studies, it was found that dangerous behaviors such as aggressive lane change required decimeter accuracy from GNSS. Consequently, in this article, the PPP (Precise Point Positioning) technique was applied to develop a behavior detection system. Vehicle maneuvers such as cornering and lane change detected by IMU and Multi-GNSS were collected and compared. The JAVAD Delta TRE_G3TH experimental Multi-GNSS receiver installed on a utility vehicle was used in this work. The navigation data were post-processed with the open source RTKLIB software. On the other hand, the reference vehicle dynamic measurement system was VBOX 3LSi with integrated IMU. A simple curvature model was proposed and applied to estimate the lateral acceleration. In conclusion, experimental results show that driver behavior detection is feasible with the Multi-GNSS PPP. The results revealed that with PPP solution the estimated lateral acceleration could be manipulated within 10% deviation.

Empowering management of a multi-section, basic engineering course with cloud storage

Cloud storage was used by lecturers for resource sharing and course management in a multi-section, mathematically intensive, basic engineering course. The storage allowed effortless desktop and mobile resource sharing between lecturers such that documents were readily accessed at any place and any time as well as reduced and decentralised workload in document management. Quizzes and exams could be set up more efficiently with feedbacks from other lecturers in a virtual group setting. The unexpected benefits included more efficient chance meetings as detailed management information was readily available, as well as a higher peer pressure among lecturers for exam settings and marking deadlines. Cloud storage also provided a supplemental channel of file distribution to students for very large files, particularly video clips. The keys to success in this adoption of more mobile learning and management were the underlying cooperation and leadership within the course management. Wide-scale adoptions could be facilitated by knowledge management activities with proven adoption model and user champions.

Evaluation of Deadband Effect in Steer-by-Wire Force Feedback System by Using Driving Simulator

This paper presents an evaluation of dead band in force feedback which affects on driving precision with Steer-by-Wire System by using Driving Simulator. Dead band is considered as a zero zone of a relation between Force feedback torque and hand wheel angle. Experimental was designed to focus on dead band size from 0 to ±10 degrees of Force feedback model. The result has shown that the driver has the best driving precision with dead band size of ±4 to ±6 degrees.

Eco-Driving Rating Based on Average Trip Speed and Evaluation

In this work, an eco-driving rating based on the relation between fuel consumption and average trip speed commonly used in traffic engineering is presented. A car following experiment with a driving simulator was used to make an eco-rating chart. The leading car was used to control average speed of each experiment. To calculate the rating, the resulting driving data was divided into trips. For each trip, low level of fuel consumption was given high score while high level was given low score. Rating was given as average of the trip scores. Actual driving data was then used for the leading car for evaluation of the rating. One of the desirable properties of the rating proposed by this work is consistency regardless of traffic conditions. As suggested by the rating chart, it was found that the proposed eco-rating is consistent in terms of ranking drivers around when average trip speed is around 50-70 km/h.