scholarly journals Modeling commercial vehicle drivers’ acceptance of advanced driving assistance system (ADAS)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yueru Xu ◽  
Zhirui Ye ◽  
Chao Wang
Keyword(s):  
Random Forests ◽  
Commercial Vehicle ◽  
Influence Factors ◽  
Assistance System ◽  
Vehicle Speed ◽  
Commercial Vehicles ◽  
Key Factors ◽  
Content Type ◽  
Driving Assistance ◽  
Driving Assistance System

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.

scholarly journals Advanced Driving Assistance System for an Electric Vehicle Based on Deep Learning

2021 ◽  
Author(s):  
Abdelaziz Sahbani ◽  
Hela Mahersia
Keyword(s):  
Electric Vehicle ◽  
Control Method ◽  
Assistance System ◽  
Speed Limit ◽  
Processing Unit ◽  
Vehicle Speed ◽  
Proposed Model ◽  
Driving Assistance ◽  
Driving Assistance System ◽  
System Controller

This chapter deals with a design of a new speed control method using artificial intelligence techniques applied to an autonomous electric vehicle. In this research, we develop an Advanced Driver Assistance System (ADAS) which aims to enhance the driving manner and the safety, especially when traveling too fast. The proposed model is a complete end-to-end vehicle speed system controller that proceeds from a detected speed limit sign to the regulation of the motor’s speed. It recognizes the speed limit signs before extracting from them, a speed information that will be sent, as reference, to a NARMA-L2 based controller. The study is developped specially for electric vehicle using Brushless Direct Current (BLDC) motor. The simulation results, implemented using Matlab-Simulink, show that the speed of the electric vehicle is controlled successfully with different speed references coming from the image processing unit.

Effect of gender and personality characteristics on the speed tendency based on advanced driving assistance system (ADAS) evaluation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cunshu Pan ◽  
Jin Xu ◽  
Jinghou Fu
Keyword(s):  
Speed Control ◽  
Assistance System ◽  
Operating Speed ◽  
Feature Points ◽  
Content Type ◽  
Control Behavior ◽  
The Poor ◽  
Driving Assistance ◽  
The Difference ◽  
Driving Assistance System

Purpose This study aims to explore the relationship between speed behavior of participants and driving styles on interchange ramps. A spiral interchange in Chongqing was selected as an experimental road to carry out field driving experiment. Design/methodology/approach The continuous operating speed during experiment was selected by Mobile Eye, and the driving style was selected via two inventories. Findings Different driving behaviors showed great differences in age, driving mileage and driving experience. During driving process, male pursued driving stimulation more, whereas female pursued driving steadiness more. Therefore, driving characteristics of male were more disadvantageous to driving safety than that of female. Except for the large speed difference at the entrance and exit of the ramps, the differences at other positions were small. And the operating speed of male was slightly higher than that of female. The difference between different genders at the ascending end position achieved 4–5 kph, and the difference at other feature points were mostly 1–2 kph. During driving process, risky participants were more likely to pursue driving stimulation, and the poor speed control behavior was reflected in wide range of desired operating speed. Based on the results of analyzing at feature points, melancholy and sanguine participants more tended to take a high operating speed, and the poor speed control behavior was reflected in the most widely desired speed range. The speed control behavior of mixed participants was more cautious. Originality/value Advanced driving assistance system combined with two inventories was used to explore difference of speed behavior.

Bus driving assistance system for town area by using ATmega328P microcontroller

2017 ◽  
Author(s):  
Mohamad Fauzi Zakaria ◽  
Tan Jiah Soon ◽  
Munzilah Md Rohani
Keyword(s):  
Assistance System ◽  
Driving Assistance ◽  
Driving Assistance System ◽  
Town Area

scholarly journals Built-in-self-test system for FPGA-based vehicle video-detection and distance measurement

2021 ◽  
Author(s):  
Md Forhad Ebn Anwar
Keyword(s):  
High Speed ◽  
Vision System ◽  
Test System ◽  
Distance Measurement ◽  
Assistance System ◽  
Project Work ◽  
Practical Calculation ◽  
Automatic Driving ◽  
Driving Assistance ◽  
Driving Assistance System

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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