Zhenhai Gao

The Research of the Driver Attention Field Modeling

For expanding the application scope of car-following, based on the basic idea of the noncontact interaction of the objects in physics, establish an attention field model to describe the driving behavior. Firstly, propose the time distance concept to describe the degree of driver perception to the front one-dimensional space and extend its application range to the two-dimensional space. Secondly, connect the point which has the same time distance to constitute the equipotential line of drivers’ attention field equipotent, and establish a model to describe it. Thirdly, define the effective range of the driver’s psychological field with the feature of the driver’s visual distance range increasing and the angle decreasing. Finally, design the calculation method to collect projection of the object in the psychological field scope and calculate the curve points to determine the object’s intensity of psychological field. Preliminarily build the driving behavior model and use the numerical simulation method to simulate the simple transport scenarios; initially verify the validity of the model.

Design of the time-gap-dependent robust headway control algorithm for ACC vehicles

A robust fuzzy headway control algorithm based on the constant time headway (CTH) model for adaptive cruise control (ACC) vehicles is developed. Considering the possible adjustment of the time gap (TG) by a driver after the activation of ACC, inter-vehicle kinematics is established by using a Takagi-Sugeno fuzzy model with the TG as the premise variable. A robust headway controller based on the model is designed by using the Lyapunov function approach. Control input saturation is considered in the design of the controller. Furthermore, the initial conditions of transitional manoeuvres from the velocity control mode to the distance control mode are computed on the basis of inter-vehicle kinematics. The designed algorithm is verified in various operating conditions. Passenger comfort can be improved by the designed algorithm under the precondition of driving safety in the tuning processes of the TG. And the individual preferences of various drivers on ACC are achieved and acceptability is eventually improved.

Human-centered headway control for adaptive cruise-controlled vehicles

Driving characteristics of human drivers, such as driving safety, comfort, handiness, and efficiency, which are interrelated and contradictory, are synthetically considered to maintain a safe inter-vehicle distance in this article. For the multi-objective coordination control problem, the safety, handiness, comfort, and efficiency indicators are established via driving states and manipulated variable. Furthermore, a multi-performance indicator coordination mechanism is proposed via the invariant set and quadratic boundedness theory. A headway control algorithm for adaptive cruise control is established under the dynamic output feedback control framework. Finally, feasibility and effectiveness of the proposed algorithm are verified via closed-loop simulations under the following, cut-out, and cut-in typical operating conditions.

Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern

Bicycle traffic has heavy proportion among all travel modes in some developing countries, which is crucial for urban traffic control and management as well as facility design. This paper proposes a real-time multiple bicycle detection algorithm based on video. At first, an effective feature called multiscale block local binary pattern (MBLBP) is extracted for representing the moving object, which is a well-classified feature to distinguish between bicycles and nonbicycles; then, a cascaded bicycle classifier trained by AdaBoost algorithm is proposed, which has a good computation efficiency. Finally, the method is tested with video sequence captured from the real-world traffic scenario. The bicycles in the test scenario are successfully detected.

Pedestrian Count Estimation Using Texture Feature with Spatial Distribution

We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.

An experimental driving simulator study of unintentional lane departure

Driving characteristics of lane departure can provide design principles for lane departure warning system and lane keeping assist system. In this article, based on driving simulator experiments of unintentional lane departure, the operation performances of human drivers, the motion characteristics of vehicles, and the relative motion between the vehicle and lane line are synthetically studied. First, unintentional lane departure is classified into lane departure by fatigue and lane departure by secondary task. Subsequently, two simulator experiments of fatigue-based lane departure and secondary task–based are designed and performed to collect the synchronous driver–vehicle–road data. The data of steering angle, steering angle velocity, steering angle entropy, lateral acceleration, lateral velocity, and yaw velocity, under fatigue-based and secondary task–based lane departure are collected and compared with those gathered under normal lane changing. Results show that the characteristics of unintentional lane departures differ from that of normal lane departure changing. Furthermore, the characteristics of fatigue-based lane departures are shown some differences with that of secondary task–based ones.

Control mode switching strategy for ACC based on intuitionistic fuzzy set multi-attribute decision making method

The control mode switching strategy is a crucial part of the Adaptive Cruise Control (ACC) decision algorithm. A novel switching strategy of control mode is proposed, which is designed using an intuitionistic fuzzy set, multi-attribute decision making method (IFSMADM). At first, three modes of cruising, following and approaching are considered for the control modes of the decision algorithm for the ACC. Then, safety performance, comfort performance and economy performance are treated as the attributes needed to assess alternative sets. Furthermore, the linear weighted average method of IFSMADM is used to achieve control mode switching. Finally, the proposed algorithm is verified based on actual road