Junqiang Xi

A novel lane detection based on geometrical model and Gabor filter

Many people die each year in the world in single vehicle roadway departure crashes caused by driver inattention, especially on the freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, in which, the lane detection is a key issue. In this paper, after a brief overview of existing methods, we present a robust lane detection algorithm based on geometrical model and Gabor filter. This algorithm is based on two assumptions: the road in front of vehicle is approximately planar and marked which are often correct on the highway and freeway where most lane departure accidents happen [1]. The lane geometrical model we build in this paper contains four parameters which are starting position, lane original orientation, lane width and lane curvature. The algorithm is composed of three stages: the first stage is called off-line calibration which just runs once after the camera is mounted and fixed in the vehicle. The parameters of camera used for lane detection is accurately estimated by the 2D calibration method [2]; The second stage is called lane model parameters estimation and lane model candidates construction, the first three parameters, starting position, lane original orientation and lane width will be estimated using dominant orientation estimation [3] and local Hough transform. Then the construction of lane model candidates is implemented for the final lane model matching; the third stage is model matching. The proposed lane module matching algorithm is implemented to match the best fitted lane model. The combination of these modules can overcome the universal lane detection problems due to inaccuracies in edge detection such as shadow of tree and passengers on the road. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self-supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data.

Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described.

Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics

To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a drivers expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the level of sensitivity to errors, i.e., low, middle, and high. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to drivers path-following characteristics, reducing the drivers workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in CarSim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed-speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, as compared with the classical control strategy of steering ratio, can improve task performance by about 7% and reduce the drivers physical workload and mental workload by about 35% and 50%, respectively, when following the given path.

Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications

The performance of energy management in hybrid electric vehicles is highly dependent on the forecasted velocity. To this end, a new velocity-prediction approach utilizing the concept of chaining neural network (CNN) is introduced. This velocity forecasting approach is subsequently used as the basis for an equivalent consumption minimization strategy (ECMS). The CNN is used to predict the velocity over different temporal horizons, exploiting the information provided through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication channels. In addition, a new adaptation law for the so-called equivalent factor (EF) in ECMS is devised to investigate the effects of future velocity on fuel economy and to impose charge sustainability. Compared with traditional adaptation law, this paper considers the impact of predicted velocity on EF. The control objective is to improve the fuel economy relative to the ECMS without considering predicted velocity. Finally, simulations are conducted in three cases over different prediction horizons to demonstrate the performance of the proposed velocity-prediction method and ECMS with adaptation law. Simulation results confirm that ECMS with EF adjusted by the proposed adaptation law produces between 0.2% and 5% improvements in fuel economy relative to ECMS with traditional adaptation law. In addition, better charge sustainability is achieved as well.

Research on Gear Shifting Process without Disengaging Clutch for a Parallel Hybrid Electric Vehicle Equipped with AMT

Dynamic models of a single-shaft parallel hybrid electric vehicle (HEV) equipped with automated mechanical transmission (AMT) were described in different working stages during a gear shifting process without disengaging clutch. Parameters affecting the gear shifting time, components life, and gear shifting jerk in different transient states during a gear shifting process were deeply analyzed. The mathematical models considering the detailed synchronizer working process which can explain the gear shifting failure, long time gear shifting, and frequent synchronizer failure phenomenon in HEV were derived. Dynamic coordinated control strategy of the engine, motor, and actuators in different transient states considering the detailed working stages of synchronizer in a gear shifting process of a HEV is for the first time innovatively proposed according to the state of art references. Bench test and real road test results show that the proposed control strategy can improve the gear shifting quality in all its evaluation indexes significantly.

Research on shift schedule of hybrid bus based on dynamic programming algorithm

In this paper, shift schedule of a hybrid bus equipped with Automated Mechanical Transmission (AMT) is researched. Although the one-parameter or two-parameter shift schedule is usually used for hybrid buses, this approach cannot make full use of the advantage of hybrid driving. A new shift schedule, which is based on Dynamic Programming algorithm, is proposed for hybrid buses, a solving algorithm is also presented. The best shifting points for the hybrid buses are obtained via the proposed shift schedule when they work in the typical urban driving-cycle conditions in China. After comparing with the traditional two-parameter shift schedule and the best fuel economy shift schedule, the best shifting points are proved to significantly improve the fuel economy of the hybrid buses and the frequent shifting situation is obviously reduced.

Optimal design and simulation evaluation of economical gear-shifting schedule for AMT in pure electronic bus

50 “zero emission” pure electrical buses BK6122EV have been running in Beijing for the whole year. As one of the core systems of the bus, the automatic mechanical transmission (AMT) has the enormous influence on its economical performance. In order to further optimize the economic gear-shifting schedule for BK6122EV, this paper uses three design methods of the conventional engine vehicle for reference, where the fuel consumption figure of the engine is substituted for the efficiency map of the driving motor. The simulation model of BK6122EV based on AVL Cruise simulation software is built to compare and optimize the designed schedules. Moreover, the best schedule is gained. The result of the simulation indicates the economical performance of the bus using the optimized schedule is improved by 1.54 percent compared with the existing schedule.

ASCS online fault detection and isolation based on an improved MPCA

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

Development of pneumatically automatic mechanical transmission for a pure electric garbage truck

The pneumatically automatic mechanical transmission (AMT) system has been developed for a pure electric garbage truck. The buildup principle and control framework of the pneumatically AMT is introduced. In order to realize the coordinate control, the CAN (Controller Area Network) bus technology is adopted to realize the communications of AMT electric control unit (ECU) and induction motor controller (MC), and then the control of the motors rotate speed and torque is realized with the instructions from AMT system. Owing to the speed control of the induction machine during the period of the gear-shifting, the synchronization is always guaranteed. Furthermore, the time of shifting to target gear is reduced greatly using the pneumatically automatic mechanical transmission system. All those shorten the whole shifting time. The experimental results show that pneumatic gear-selecting and gear-shifting is quick and reliable and proposed pneumatically AMT improves some performances of the vehicle.