Duanfeng Chu

Risk Prediction for Curve Speed Warning by Considering Human, Vehicle, and Road Factors

Current curve speed warning systems take into account mostly vehicle and road factors but not driver behavior. The systems aim at detecting sideslips of small cars on curves without consideration of rollovers for vehicles with an elevated center of gravity. In this study, a curve speed model that considers human, vehicle, and road factors is built to prevent not only sideslips but also rollover accidents for vehicles with an elevated center of gravity. In addition, a risk prediction model is presented to judge accident risk levels and determine levels of warning. Finally, the effectiveness of the presented system is validated with one skilled driver who carries out one test through a simulator under different curve scenarios. To verify the system, data from simulator tests were collected for offline checking of the system. The data were used to calculate safe speeds by using the curve speed model and to determine the levels of risk based on the risk prediction model. The results show that the system is highly compatible with the skilled driver in terms of warning accuracy and timing. Specifically, the correct alarm rate (i.e., the driver brakes and the system’s alarm goes off) of the system is 83.57% and the error alarm rate (i.e., the driver does not brake but the system’s alarm goes off) is 9.79%. Moreover, more than 80% of the time the difference between the system warning time and the operating time of the skilled driver is less than 2 s.

Clustering of Several Typical Behavioral Characteristics of Commercial Vehicle Drivers Based on GPS Data Mining: Case Study of Highways in China

Some studies of driving behavior have been based on data mining to create a mechanism that relates data derived from vehicle monitoring, driver behavioral characteristics, and road safety to each other. To make the best of GPS data collected by transportation businesses and explore the potential rules of commercial vehicle driver behavioral characteristics, the parameters related to driving behavioral characteristics are extracted according to GPS data attributes based on factor analysis, and eight parameters of driving behavioral characteristics are transformed into a few aggregated variables containing clear information about driving behavior. With these variables as indicators, a cluster analysis of commercial vehicle driver behavioral characteristics in the selected case is carried out through hierarchical clustering. The results show that commercial vehicle driver behavioral characteristics can be effectively aggregated into four kinds: acceleration–deceleration, speeding-prone, acceleration, and deceleration. Of the four kinds, drivers with relatively serious acceleration–deceleration behavior are also characterized by three other relatively serious behaviors; such drivers have relatively high driving risks, so transportation businesses need to focus their supervision on those drivers. The research results have some relevance to the supervision and training of commercial vehicle drivers in China.

Contribution of wind forces to rollover stability of heavy duty vehicle

HDVs rollovers cost hundreds of million dollars every year, the risk of having dead people in accidents involving HDVs is multiplied by 2.4 in comparison to the same risk computed for accidents involving light vehicles. Wind has been shown to be an important factor that must be considered when investigating HDVs rollover. In order to address these concerns, this research built an aerodynamic and vehicle dynamic model. In addition, the vehicle dynamic model, wind model and road model were simulated by TruckSim. The results of the analysis showed that wind speed had an influence on the rollover stability experienced by a bus. The simulation results were based on both wind speed and vehicle speed. It has been shown that as wind speed increased, the roll angle and yaw angle experienced by the bus increased, and the peak roll angle showed increases as wind direction increased. The contribution of this paper could help in implementing design standards for highways or wind barriers, and in the design of vehicles themselves.

A Probabilistic Prediction Model for the Safety Assessment of HDVs Under Complex Driving Environments

Accidents such as those caused by rollovers and sideslips in complex driving environments involving heavy-duty vehicles (HDVs) often have serious consequences. Such accidents can be due to many factors. In this paper, a probabilistic method for predicting and preventing these accidents is presented. First, a specific vehicle dynamics model based on various random parameters that consider the wind velocity and road curvature is developed. Second, a safety margin function is defined to divide the safe and dangerous domains in the parameter space. Then, the first-order reliability method and second-order reliability method approximations are developed to evaluate the probability of such an accident by using the vehicle dynamics model. Finally, the probability model is applied to explore the interrelations and sensitivities of those parameters with regard to their effects on the accident probability in different scenarios. The study suggests that the presented probabilistic methodology can effectively estimate rollovers and sideslips of HDVs in complex environments, which represent a challenge for the prediction of accidents based on sensors alone.

Smooth Sliding Mode Control for Vehicle Rollover Prevention Using Active Antiroll Suspension

The rollover accidents induced by severe maneuvers are very dangerous and mostly happen to vehicles with elevated center of gravity, such as heavy-duty trucks and pickup trucks. Unfortunately, it is hard for drivers of those vehicles to predict and prevent the trend of the maneuver-induced (untripped) rollover ahead of time. In this study, a lateral load transfer ratio which reflects the load distribution of left and right tires is used to indicate the rollover criticality. An antiroll controller is designed with smooth sliding mode control technique for vehicles, in which an active antiroll suspension is installed. A simplified second order roll dynamic model with additive sector bounded uncertainties is used for control design, followed by robust stability analysis. Combined with the vehicle dynamics simulation package TruckSim, MATLAB/Simulink is used for simulating experiment. The results show that the applied controller can improve the roll stability under some typical steering maneuvers, such as Fishhook and J-turn. This direct antiroll control method could be more effective for untripped rollover prevention when driver deceleration or steering is too late. It could also be extended to handle tripped rollovers.

Probabilistic assessment for HDVs rollover using second order reliability method

The rollover accidents under complex driving environments involving heavy-duty vehicles (HDVs) often lead to serious consequences. For predicting rollover accidents, a probabilistic assessment model is proposed. Firstly, a specific vehicle dynamic model is developed with some random parameters taking into account wind velocity and road curvature. Then, the SORM (Second Order Reliability Method) approximations are developed to evaluate the probability of rollover accidents over the vehicle dynamics model. Finally, the probability model is applied to explore the interrelation and sensitivity of those parameters for their effects on the probability of the accident in different scenarios. The study suggests that the probabilistic methodology presented could be effective in estimating HDVs rollover under complex environment situations.

Online estimation for vehicle center of gravity height based on unscented Kalman filter

The Center of Gravity height of heavy-duty vehicles will drift apparently when the load changes, while online estimation of the vehicle CG height is of high importance to the vehicle active safety system. An unscented Kalman filter based on three-degree-of-freedom (3-DOF) vehicle dynamics model is proposed to acquire the real-time value of vehicle CG height through sensing vehicle speed, front and rear wheel speed etc. The results of combined simulation studies based on TruckSim and MATLAB/Simulink show that the estimation algorithm is able to obtain the true value of CG height in a short time with a steady average error rate less than 12%. The results are instructive for the vehicle dynamic control system.

Vehicle Trajectory Restoration Based on Vondrak Filtering and Cubic Spline Interpolation

Vehicle trajectory records the vehicle location and time sequence, which is significant for the travel state analysis and road accidents forecast. In order to repair the fault data of vehicle location and time updates resulted from global position system (GPS) data transmission interruption and resolve problems of vehicle trajectory information lack. This paper studies vehicle trajectory restoration, designing a distributed model for vehicle restoration trajectory, using Vondrak filtering to smooth the travel trajectory and then repairing latitude and longitude information by using cubic spline interpolation. The vehicle trajectory can be got by the merger of latitude and longitude information. Restoration methods are verified by MATLAB under simulation. The results show that the algorithm error is 6.01×10-5, which is able to guarantee the accuracy of vehicle trajectory information and provide relatively accurate data to support researches based on travel trajectory.

Identification of Human Failures in the Lane Changing Process on Expressways Based on Cepstral Analysis

Accidents can be seen as the result of an unsuccessful interplay between drivers, vehicles, traffic environments, and organizations. Human actions are an important source of vulnerability for road transportation. Current research about lane changing focuses on driving intentions. Few studies are focused on human failures during lane changing processes. In this paper, based on cepstral analysis, the characteristics of steering operation are studied to research driving failure characteristics during the lane changing processes. Then, a BP neural network model of lane changing is established to identify driving failures in lane changing processes. The identification rate of this model is 70%, which can be used to improve driving safety.

A novel rollover warning method for ground vehicles based on smartphone built in GIS/GPS

In recent years, it has been shown that damages of casualties and property losses caused by vehicle rollover accidents are severe. Vehicle rollovers can easily cause secondary accidents such as chain collisions on freeways. Ground vehicles with relatively higher center of gravity on sharp curves are prone to rollover accidents. Therefore, it is of vital significance to design a rollover warning system for this type of vehicles. After summarizing current research achievements about vehicle rollover warning methods, the paper presents a novel rollover warning method for ground vehicles based on GIS/GPS. It firstly elaborates the method for road curve identification and curve radius estimation. Then, a curve speed model is built based on vehicle dynamics and road environment conditions. Lastly, it presents the architecture and field test verification of the rollover warning system. The main contributions of this paper are organized as follows: 1) Curve speed model is built by analyzing the correlation between the front wheel angle and the curve radius. Moreover, rollover limiting condition is analyzed based on vehicle roll dynamics modeling; 2) Curve road recognition based on GIS/GPS using smartphone platform. It presents a curve recognition algorithm based on acquiring road and vehicle parameters such as curve entry, driving direction and vehicle lateral acceleration; 3) Curve radius estimation based on road curve fitting and radius calculation. After obtaining location of each circular section on the curve through the digital map, the center of each circular section is determined and then the average radius of the curve is calculated.