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Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal, the accident can be avoided or not by the driver when reacting to this signal.

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario, and driving risk perception is based on the ICV technology. In this way, trajectories of surrounding vehicles can be predicted and tracked in a real-time manner. In this paper, vehicle dynamics based state-space equations conforming to model predictive controllers are set up to primarily explore and identify a safety domain of active collision avoidance. Furthermore, the model predictive controller is also designed and calibrated, thereby implementing the active collision avoidance strategy for vehicles based on the model predictive control method. At last, functional testing is conducted for the proposed active collision avoidance control strategy in a designed complex traffic scenario. The research findings here can effectively improve automatic driving, intelligent transportation efficiency and road traffic safety.

Vehicle collisions with wild animals are a common problem on roads, having a significant impact on road safety and wildlife populations. Collisions with wild animals are one of the most frequent road accidents. According to police statistics, there were nearly 16 000 road accidents caused by collisions with animals in the Czech Republicin 2019. Collisions with deer are the most common. There are several technologies and measures that can help reduce the risk of a vehicle colliding with a wild animal. One of the technologies used is a night vision system based on infrared spectrum sensing. This technology is slowly becoming part of the equipment of, in particular, premium car brands dueto its high cost. This paper tested a low-cost solution using a commercially available thermal imaging camera and found a substantial reduction in the time to detect wild animals along the road, namely in the order of seconds.

Vehicle rollover, and its resulting fatalities, is an actively researched topic especially for multi-axle vehicles in the field of vehicle dynamics and control. This paper first presents a new rollover index for a triaxle bus to accurately evaluate its rollover possibility and then discusses the influence laws of the vehicle rollover dynamics to explore the mechanism of its stability. First, a six degree of freedom rollover model of the triaxle bus is developed, including lateral, yaw, roll motion of the sprung mass of the front/rear axle, and roll motion of the unsprung mass of the front/rear axle. Next, some key parameters of the vehicle rollover model are identified. A new rollover index is deduced according to the basics of vehicle dynamics, to predict vehicle rollover risk for the triaxle bus, which is verified by TruckSim. Furthermore, the influence laws of vehicle rollover dynamics by vehicle parameters and road parameters are discussed based on the simulation results. More importantly, the results show that the new method of modeling can precisely describe the rollover dynamics of the studied bus, and the proposed new index can effectively evaluate the rollover possibility. Therefore, this study provides a theoretical basis to improve anti-rollover ability for triaxle buses.

Dangerous driving behavior is the leading factor of road traffic accidents; therefore, how to predict dangerous driving behavior quickly, accurately, and robustly has been an active research topic of traffic safety management in the past decades. Previous works are focused on learning the driving characteristic of drivers or depended on different sensors to estimate vehicle state. In this paper, we propose a new method for dangerous driving behavior prediction by using a hybrid model consisting of cloud model and Elman neural network (CM-ENN) based on vehicle motion state estimation and passenger’s subjective feeling scores, which is more intuitive in perceiving potential dangerous driving behaviors. To verify the effectiveness of the proposed method, we have developed a data acquisition system of driving motion states and apply it to real traffic scenarios in ShenZhen city of China. Experimental results demonstrate that the new method is more accurate and robust than classical methods based on common neural network.

Emotion is the external way to express human’s inner thoughts, which has a significant influence on human behaviors. It is an important prerequisite for studying the intrinsic affect mechanism of emotions on behaviors certain. In this article, drivers’ emotional induction experiment, actual and virtual driving experiments are designed to obtain the multi-source dynamic data of human–vehicle–environment under the condition of different emotions. The influences of emotions’ changes on car movement characteristics of different types of drivers are explored. Changing law of car movement characteristics under the condition of different emotions can be obtained finally. The research can provide theoretical basis for the future research of driver assistance system, which is of great significance to realize active vehicle safety warning and unmanned driving in the future.

Driving emotion plays an important role in intention generation and behavior decision. It is an important premise to understand the influencing mechanism of driving emotion on intention correctly for the realization of driving intention identification, active vehicle security warning, and intelligent driving. In the case of two-lane, multi-source dynamic data of human–vehicle–environment under drivers’ different emotional states is obtained through actual and virtual driving experiment in this article. Considering the influence of emotion on intention, the probability distributions of the driving intention in different emotions are obtained by means of the immune algorithm. And the priming effect of vehicle driving emotion on intention was analyzed deeply. The research results can provide theoretical foundation for the research of driving intention identification, active vehicle security warning system, and intelligent driving command system under the condition of Internet of things.

In this paper, a novel attitude estimation approach for the vehicle using a modified invasive weed optimized particle filter is studied, and it can be applied to provide the vehicle angle accurately. A modified invasive weed optimization (MIWO), being added a control envelope in the standard deviation of the invasive weed optimization (IWO) to improve the convergence speed and avoid the conventional IWO plunging in the local optimal solution, is introduced in the sampling process of the particle filter. The proposed particle filter makes particles reproduced dynamically by their fitness in a nearby space, and optimizes the particle population of optimal weights to let particles move towards the regions where they have the great values of the posterior probability density. Within the framework of the proposed particle filter, the modified Rodrigues parameters (MRPs) associated with the angular velocity of a gyro are included in a state vector, and the outputs of the accelerometer and magnetometer are regarded as the observation vector which is used for correcting the errors of the angular velocity measurement. The simulation results show that the suggested particle filter can enhance the accuracy of the state estimation compared with other improved particle filters. The experimental results prove that the attitude errors using the presented method are significantly reduced by comparing to that obtained from the extended Kalman filter and the classic particle filter.

As driver assistant systems (DAS) and active safety vehicles (ASV) with various functions become popular, it is not uncommon for multiple systems to be installed on a vehicle. If each function uses its own sensors and processing unit, it will make installation difficult and raise the cost of the vehicle. As a countermeasure, research integrating multiple functions into a single system has been pursued and is expected to make installation easier, decrease power consumption, and reduce vehicle pricing. This paper proposes a novel side/rear safety system using only one scanning laser radar, which is installed in the rear corner of the driver’s side. Our proposed system, ISRSS (integrated side/rear safety system), integrates and implements four system functions: BSD (blind spot detection), RCWS (rear collision warning system), semi-automatic perpendicular parking, and semi-automatic parallel parking. BSD and RCWS, which operate while the vehicle is running, share a common signal processing result. The target position designation for perpendicular parking and parallel parking situations is based on the same signal processing. Furthermore, as system functions during running and those during automatic parking operate in exclusive situations, they can share common sensors and processing units efficiently. BSD and RCWS system functions were proved with 13025 and 2319 frames, respectively. The target position designation for perpendicular and parallel parking situations was evaluated with 112 and 52 situations and shows a success rate of 98.2% and 92.3%, respectively.