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

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.

CASA: An Alternative Smartphone-Based ADAS

Advanced driving assistance system (ADAS) is an electronic system that helps the driver navigate roads safely. A typical ADAS, however, is suited to specific brands of vehicle and, due to proprietary restrictions, has non-extendable features. Project CASA is an alternative, low-cost generic ADAS. It is an app deployable on smartphone or tablet. The real-time data needed by the app to make sense of its environment are stored in the vehicle or on the cloud, and are accessible as web services. They are used to determine the current driving context, and, if needed, decide actions to prevent an accident or keep road navigation safe. Project CASA is an undertaking of a consortium of industrial and academic partners. A use case scenario is tested in the laboratory (virtual) and on the road (actual) to validate the appropriateness of CASA. It is a contribution to safe driving. CASA’s contribution also lies in its approach in the semantic modeling of the context of the environment, the vehicle and the driver, and on the modeling of rules for fusion of data and fission process yielding an action to be implemented. In addition, CASA proposes a secured means of transmitting data using light, via light fidelity (LiFi), itself an alternative means of wireless vehicle–smartphone communication.

Validation and Optimization of Suspension Design for Testing Platform Vehicle

With the application and popularization of the advanced driving assistance system (ADAS), the reliability and stability of ADAS have become its research focus. This article presents a car testing framework for ADAS reliability and stability. Its special suspension has been designed, verified, and optimized in real vehicles according to its working conditions. First, the structure and working principle of the testing platform vehicle are introduced. Then a simulation model is built in MATLAB/Simulink based on the dynamic equation to verify the working characteristics of the suspension. Experimental vehicle tests are conducted for simulation verification purposes. During the analysis, the root-mean-square (RMS) values of vehicle body displacement and dynamic tire deflection are considered evaluation indices. The nondominated sorting genetic algorithm (NSGA-II) is used to optimize the damping, stiffness, and installation position of the suspension system. The findings demonstrate that the specially designed suspension in this article can fulfill the test criteria. Compared with the optimized suspension performance, both the vehicle body displacement and dynamic tire deflection have decreased roughly by 17 and 40%, respectively, which significantly improves the suspension performance and provides a reference for the future designs of testing platform vehicles.

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

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.

Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Road crashes cause serious loss of life and property. Among all vehicles, buses are more likely to encounter crashes. In recent years, the advanced driving assistance system (ADAS) has been widely used in buses to improve safety. The warning system is one of the key functions and has proven effective in reducing crashes. However, drivers often ignore or overreact to ADAS warnings during naturalistic driving scenarios. Therefore, reactions of bus drivers to warnings need further investigation. In this study, bus drivers’ responses to lane departure warning (LDW) and forward collision warning (FCW) were investigated using 20-day naturalistic driving data. These reactions could be classified into three categories, namely positive, negative, and overreaction or emergency, by employing the Gaussian mixture model. The authors constructed a framework to quantify drivers’ reactions to the warning and study the reaction characteristics in different environments. The results indicate that drivers’ reactions to FCW were more positive than to LDW, drivers reacted more positively to LDW and FCW while driving on highways than on urban roads, and drivers reacted more positively at night to LDW and FCW than during daytime. This study gives support to an adaptive ADAS considering varying bus driver characteristics and environments.

Driving Assistance System for Security and Safety

The increase in the number of vehicles increases the number of accidents. India ranks first in the world by 11% where a greater number of accidents occur followed by China and the USA. Most road accidents occur due to human errors. The Advanced Driver Assistant System is developed to Automate, Adapt and increase the safety of the vehicle and driver and also to ensure better driving. The safety features in this system are designed and developed to avoid collision by using technologies that give an indication or alert to the driver regarding problems, implementing safe grounds, and controlling the vehicle if necessary. In recent years Ultrasonic sensor-based systems and the rear-view camera-based system is been used by different car manufacturers which are available only on higher-end vehicles. In the proposed system we calculate the frontal road width by capturing an image using a Vehicle Frontal Camera(VFC) and processing it for proper judgment of space available for the vehicle to pass, with that surround space detection is achieved by using LIDAR and detection of unattended pets/child inside the vehicle by using IR proximity sensors and identification of pets/objects under the vehicle using Chassis level camera when parked in a remote location or cities to avoid loss of life of animals and to avoid the damage caused to the vehicle.