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.

Methodical Approach to Integrate Human Movement Diversity in Real-Time into a Virtual Test Field for Highly Automated Vehicle Systems

Recently, virtual realities and simulations play important roles in the development of automated driving functionalities. By an appropriate abstraction, they help to design, investigate and communicate real traffic scenario complexity. Especially, for edge cases investigations of interactions between vulnerable road users (VRU) and highly automated driving functions, valid virtual models are essential for the quality of results. The aim of this study is to measure, process and integrate real human movement behaviour into a virtual test environment for highly automated vehicle functionalities. The overall system consists of a georeferenced virtual city model and a vehicle dynamics model, including probabilistic sensor descriptions. By motion capture hardware, real humanoid behaviour is applied to a virtual human avatar in the test environment. Through retargeting methods, which enable the independency of avatar and person under test (PuT) dimensions, the virtual avatar diversity is increased. To verify the biomechanical behaviour of the virtual avatars, a qualitative study is performed, which funds on a representative movement sequence. The results confirm the functionality of the used methodology and enable PuT independence control of the virtual avatars in real-time.

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Purpose–The purpose of this paper is to develop a proof-of-concept(POC)Forward Collision Warning(FWC)system for the motorcyclist,which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle(motorcycle).Design/methodology/approach–This comes in three approaches.First,time-to-collision value is to be calculated based on low-cost camera video input.Second,the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate.Third,the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.Findings–This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above.First,to predict time-to-collision,nested Kalmanfilter and vehicle detection is used to convert image pixel matrix to relative distance,velocity and time-to-collision data.Next,for trajectory prediction of detected vehicles,a few algorithms were compared,and it was found that long short-term memory performs the best on the data set.The lastfinding is that to determine the leaning direction of the ego vehicle,it is better to use lean angle measurement compared to riding pattern classification.Originality/value–The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle(motorcycle).