Search results

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

The purpose of this paper is to present a prototype simulation system for driving performance of an electromagnetic unmanned robot applied to automotive test (URAT) to solve that it is difficult and dangerous to online debug control program and to quickly obtain test vehicle dynamic performance.

The driving performance of the electromagnetic URAT can be evaluated by the prototype simulation system. The system can simulate various driving conditions of test vehicles. An improved vehicle longitudinal dynamics model matching to the electromagnetic URAT is established. The proposed model has good real-time, and it is easy to implement. The displacement of throttle mechanical leg, brake mechanical leg, clutch mechanical leg and shift mechanical arm is used for the system input. Test vehicle speed and engine speed are used for the system output, and they are obtained by the computation of the established vehicle longitudinal dynamics model.

Driving conditions simulation test and vehicle emission test are performed using a Ford Focus car. Simulation and experiment results show that the proposed prototype simulation system in the paper can simulate the driving conditions of actual vehicles, and the performance that electromagnetic URAT drives an actual vehicle is evaluated by the simulation system.

Future research will focus on improving the real time of the proposed simulation system.

The autonomous driving performance of electromagnetic URAT can be evaluated by the proposed prototype simulation system.

A prototype simulation system for driving performance of an electromagnetic URAT based on an improved vehicle longitudinal dynamics model is proposed in this paper, so that it can solve the difficulty and danger of online debugging control program, quickly obtaining the test vehicle performance.

The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.

The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver's driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors.

This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density.

The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects.

Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems.

Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving.

Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system.

A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI.

This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles.