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.

CVTD: A Robust Car-Mounted Video Text Detector

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.

On the usefulness of an assisted driving Hamiltonian for quantum adiabatic evolution

In our recent work we showed, by investigating the initialization of some unusual forms of assisted driving Hamiltonians, that the addition of an assisted driving Hamiltonian is not always useful in quantum adiabatic evolution. These unusual forms are those that are not the relatively fixed ones that are widely used in the literature. In this paper, we continue this study, providing further evidence for the validity of the conclusion above by researching some relatively more complex forms of assisted driving scheme, which generalize the ones studied in our previous work.

A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways.To enhance compliance with recommended speeds,the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal.Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales,this study proposes such a guidance system.Initially,we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp,thereby facilitating harmonized speed and spacing strategies.Subsequently,we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner.Lastly,we investigate a closed-loop feedback approach for speed guidance that incorporates the driver’s execution rate,thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline.Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.

Intelligent Speed Limit System for Safe Expressway Driving in Rainy and Foggy Weather Based on Internet of Things

The feature bends and tunnels of mountainous expressways are often affected by bad weather,specif-ically rain and fog,which significantly threaten expressway safety and traffic efficiency.In order to solve this problem,a vehicle–road coordination system based on the Internet of Things(IoT)is developed that can share vehicle–road information in real time,expand the environmental perception range of vehicles,and realize vehicle–road collaboration.It helps improve traffic safety and efficiency.Further,a vehicle–road cooperative driving assistance system model is introduced in this study,and it is based on IoT for improving the driving safety of mountainous expressways.Considering the influence of rain and fog on driving safety,the interaction between rainfall,water film,and adhesion coefficient is analyzed.An intelligent vehicle–road coordination assistance system is constructed that takes in information on weather,road parameters,and vehicle status,and takes the stopping sight distance model as well as rollover and sideslip model as boundary constraints.Tests conducted on a real expressway demonstrated that the assistance system model is helpful in bad weather conditions.This system could promote intelligent development of mountainous expressways.

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.

Speed control strategy for tractor assisted driving based on chassis dynamometer test

Realizing automation of the chassis dynamometer and the unmanned test workshop is an inevitable trend in the development of new tractor products.The accuracy of the speed control of the test tractor directly affects the accuracy of the test loading force.In order to meet the purpose of precise control of the test tractor speed on the chassis dynamometer,a fuzzy PID control strategy was developed according to the working principle of assisted driving.On the basis of traditional PID control,the parameters of fuzzy inference module were added for real-time adjustment to achieve faster response to tractor speed changes and more precise control of tractor speed.The Matlab-Cruise co-simulation platform was established for simulation,and the experiment was verified by the tractor chassis dynamometer using the NEDC working condition and tractor ploughing working condition.The results show that both PID control and fuzzy PID control can achieve tractor speed following accuracy of±0.5 km/h.Fuzzy PID control has higher tractor speed following accuracy,faster response when speed changes,less tractor speed fluctuation,and overall control effect is better than PID control.The research results can provide a reference for the realization of the chassis dynamometer unmanned test workshop.

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).