Evaluation of driving behavior and the efficacy of a predictive eco-driving assistance system for heavy commercial vehicles in a driving simulator experiment

Author(s):  
Thomas J. Daun ◽  
Daniel G. Braun ◽  
Christopher Frank ◽  
Stephan Haug ◽  
Markus Lienkamp
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yueru Xu ◽  
Zhirui Ye ◽  
Chao Wang

Purpose Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper. Design/methodology/approach A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered. Findings The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system. Originality/value Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.


2013 ◽  
Vol 49 (Supplement) ◽  
pp. S264-S265
Author(s):  
Mitsuhiko KARASHIMA ◽  
Hiromi NISHIGUCHI

2017 ◽  
Author(s):  
Mohamad Fauzi Zakaria ◽  
Tan Jiah Soon ◽  
Munzilah Md Rohani

2021 ◽  
Author(s):  
Md Forhad Ebn Anwar

Collision of vehicles in highways are very frequent. Because of high speed (more than 100 km/hour), the momentum of collision is too high that leads sever casualty. Automatic Driving Assistance system can assist the vehicle operators to take decision based on realistic practical calculation on safety measures. It is always better to have third eye working parallel with human to avoid road accident. There are several technologies used to develop perfect driving assistance system to achieve higher accuracy in detection, identification and distance measurement of obstacles where vision based system is one of them. Mono-vision system provides cheap and fast solution rather stereo vision. This project work conducted with objective to comprehend computational complexity in implementation of mono-vison camera based object detection where system will generate warning if the detected object has a motion towards target. Processing and analyzing of captured video image is the focused mechanism of implementation and used internal image generator module to mimic actual video camera. Appeared size of the shape of object considered for the decision making. The simulated image pattern can change it’s dimension to represent vehicle movement in one direction (Back and forth). In this work the on-chip car image generation sub-system was proposed designed and partially implemented on the base of the FPGA where Xilinx Zynq-7010 (ZYNQ XC7Z010-1CLG400C) FPGA development board used. Keyword: Computer Vision, mono vision, image processing on FPGA, Automatic Driving Assistance, Vehicle Detection.


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