Front vehicle detection based on improved fusion method for lidar and visual image

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

Dimensioning of a permanent magnet synchronous machine for electric vehicles according to performance and integration requirements

Finding the optimum design of electrical machines for a certain purpose is a time-consuming task. First results can be achieved, however, with scaling known machine designs in length and turns per coil by means of analytical equations, while scaling in diameter requires finite element analysis (FEA), since electromagnetic properties change significantly. In this paper, the influence of diameter, length and turns per coil on the torque, power and efficiency of a permanent magnet synchronous machine (PMSM) are investigated in a sensitivity analysis. Furthermore, their impact on energy consumption in different drive cycles and different vehicle types is outlined. A highway car and a city car are compared in a highway cycle, a city cycle and the Worldwide Harmonized Light Vehicle test Cycle. The results describe significant differences in energy consumption for different machine designs in one application but also between different applications. This highlights the necessity to decide whether or not the powertrain should be optimized for a single purpose or for universal use.

A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information

To achieve the real-time application of a dynamic programming (DP) control strategy, we propose a predictive energy management strategy (PEMS) based on full-factor trip information, including vehicle speed, slip ratio and slope. Firstly, the prediction model of the full-factor trip information is proposed, which provides an information basis for global optimization energy management. To improve the prediction’s accuracy, the vehicle speed is predicted based on the state transition probability matrix generated in the same driving scene. The characteristic parameters are extracted by a feature selection method taken as the basis for the driving condition’s identification. Similar to speed prediction, regarding the uncertain route at an intersection, the slope prediction is modelled as a Markov model. On the basis of the predicted speed and the identified maximum adhesion coefficient, the slip ratio is predicted based on a neural network. Then, a predictive energy management strategy is developed based on the predictive full-factor trip information. According to the statistical rules of DP results under multiple standard driving cycles, the reference SOC trajectory is generated to ensure global sub-optimality, which determines the feasible state domain at each prediction horizon. Simulations are performed under different types of driving conditions (Urban Dynamometer Driving Schedule, UDDS and World Light Vehicle Test Cycle, WLTC) to verify the effectiveness of the proposed strategy.

Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles, Lidars are accurate in determining objects’ positions but significantly less accurate as Radars on measuring their velocities. However, Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. In order to compensate for the low detection accuracy, incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR, in this paper, an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles. By employing the Unscented Kalman Filter, Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle. Finally, the real vehicle test under various driving environment scenarios is carried out. The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy. Compared with a single sensor, it has obvious advantages and can improve the intelligence level of autonomous cars.

Tractor-trailer-train braking time sequence detection based on monocular vision

The tractor-trailer-train at the braking process prone to braking instability caused by asynchronous braking between the shafts. With respect to the lack of intelligent detection of Braking Time Sequence (BTS), a non-contact dynamic detection scheme of intelligent vehicle BTS is proposed. Based on the monocular vision principle, the edge markers of tractor-trailer train tires are identified, and the tire slip rate is solved. The noise reduction of the collected image is processed. The marker area is obtained by Blob analysis. This region at the image to be matched is identified by the template matching algorithm based on contour. The camera is calibrated by Zhang’s calibration method. In order to verify the effectiveness of the detection scheme, the real vehicle test was carried out. The test results show that the error of slip rate solution is below 4.2%.

Research on Truck Traffic Volume Conditions of Auxiliary Lanes on Two-Lane Highways

The larger the proportion of truck traffic volume, the greater the impact on traffic efficiency, and overtaking behavior will also have an impact. Therefore, in order to clarify the truck traffic volume of the freight two-lane highway due to the difficulty of overtaking, an actual vehicle test is carried out. This involves selecting the appropriate two-lane test section, recording each moment and speed in the driver’s overtaking behavior, performing multiple regression analysis to examine the relationship between the overtaking conflict time and design speed and traffic volume, determining a reasonable evaluation series of two-lane road overtaking risk and the corresponding overtaking conflict time threshold by the Fisher optimal segmentation method, and giving an overtaking behavior risk evaluation method based on conflict time. Finally, according to the overtaking conflict time model, different truck traffic conditions are obtained. The research results show that overtaking conflict time is negatively correlated with the traffic volume and design speed of the lane. Through the risk assessment of the corresponding overtaking behavior, the three levels of serious conflict, general conflict and non-conflict are determined, and the freight traffic volume corresponding to different conflict levels at different speeds is calculated, which provides a reference for setting auxiliary lanes for the two-lane freight highway.

Study on FE-SEA Modeling and Acoustic Performance of Heavy Duty Commercial Vehicle Based on Experimental Statistical Energy Parameters

Due to the difficulty of obtaining statistical energy parameters of complex structures and the complexity of modeling connection and model verification, the hybrid FE-SEA model has many problems in modeling complex structures. Therefore, in order to solve the above problems, this paper provides a reference for the application of the hybrid FE-SEA model in complex structures. In this paper, the hybrid FE-SEA commercial vehicle model is established by an experimental statistical energy parameter modeling method and a modification method. The model division and subsystem connection modeling of a complex substructure of a heavy vehicle cab are studied. In the hybrid model, the hybrid line connection and the hybrid point connection are established. On this basis, the parameters of the cab model were studied, and the statistical energy parameters such as modal density, internal loss factor, and coupling loss factor were obtained by the experimental method. The statistical energy parameters of the cab acoustic model are modified. Finally, the accuracy of the model is verified by vehicle test. In addition, the acoustic performance of the cab was optimized, and airtightness and acoustic packaging were verified. The full parameter modeling and correction method is adopted in this paper, which is an effective supplement to the traditional statistical energy parameter modeling method.

Stability control of steer by wire system based on improved ADRC

In this paper, a stability control method based on active front steering control is proposed to steer by wire system (SBW). The proposed control system consists of an inner-loop angle tracking controller and an outer-loop controller. The inner-loop controller contributes to front steering angle tracking, and it is designed by PID control. The outer-loop controller restrains the effect of disturbance by feeding a compensation steering angle, and it is designed by using Improved Active Disturbances Rejection Control (IADRC) with less adjusted parameters than Active Disturbances Rejection Control (ADRC). Finally, the effectiveness of the proposed method is evaluated via numerical simulation and vehicle test. The obtained results show that the proposed controller can improve the handling stability of the SBW system. To a certain extent, the study results promote the research and application of SBW system.

Research on Cooperative Braking Control Algorithm Based on Nonlinear Model Prediction

To address the coordinated distribution of motor braking and friction braking for the regenerative braking system, a cooperative braking algorithm based on nonlinear model predictive control (NMPC) is proposed, with braking energy recovery power, tire slip rate, and motor torque variation as the optimization objectives, and online optimization of the coordinated distribution of motor braking and friction braking. Using the offline model built in Matlab/Simulink, the cooperative braking algorithm is tested for energy efficiency and braking safety. The results show that when based on World Light Vehicle Test Cycle (WLTC), the energy recovery rate can reach 30.4%, and with a single high braking intensity, the braking safety can still be ensured.