Measurement of Particle Dynamics on the Real Vehicle in Different Driving Scenarios with Swarm Sensors

2021 ◽  
Author(s):  
Georg-Peter Ostermeyer ◽  
Guido Lehne-Wandrey ◽  
Malte Sandgaard
Keyword(s):  
Particle Dynamics ◽  
The Real ◽  
Real Vehicle

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

2022 ◽  
pp. 095440702110685
Author(s):  
Aijuan Li ◽  
Zhenghong Chen ◽  
Donghong Ning ◽  
Xin Huang ◽  
Gang Liu
Keyword(s):  
Average Method ◽  
Weighted Average ◽  
Visual Image ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
The Real ◽  
Weighted Average Method ◽  
Weighted Fusion ◽  
Vehicle Test ◽  
Real Vehicle

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.

A Research on the Application of Kohonen Neural Network to the Corrosion and Testing of Vehicle Equipment

2013 ◽  
Vol 325-326 ◽  
pp. 875-878
Author(s):  
An Tao Xu ◽  
Bing Luo ◽  
Fan Zhang ◽  
Fu Jin ◽  
Tian Ru Zhang
Keyword(s):  
Neural Network ◽  
Convenient Method ◽  
Characteristic Parameter ◽  
The Real ◽  
Kohonen Neural Network ◽  
The Neural Network ◽  
Vehicle Testing ◽  
Protective Performance ◽  
Real Vehicle

Based directly on the neural network weights from testing and evaluating the coatings on real vehicle equipment, by inputting the values of the EIS characteristic parameter received from the real vehicle testing, memorizing the neural network weights under guideless training, it provides a quick and convenient method to evaluate the protective performance of vehicle equipment coatings, and thus the quick testing of the corrosion severity of vehicle equipment can be achieved.

The Development of Air Suspension Controller in Mechanical Engineering Based on DSP

2013 ◽  
Vol 644 ◽  
pp. 101-104
Author(s):  
Zhen Wei Zhang ◽  
Chong Chen ◽  
Ruo Bing Jiao ◽  
Rong Rong Hu
Keyword(s):  
Mechanical Engineering ◽  
Vehicle Model ◽  
Air Spring ◽  
Rolling Angle ◽  
Matlab Simulink ◽  
The Real ◽  
The Core ◽  
Air Suspension ◽  
Vehicle Test ◽  
Real Vehicle

Cylindrical air spring suspension’s vehicle model with seven freedom degrees is established. Then the model is simulated by use of Matlab/Simulink to accomplish the simulated-computation of the driving state such as rolling angle and pitching angle. Based on the above work, air suspension controller, DSP TMS320F2812 chip as the core processor, is developed. The result of the real vehicle test proves that the controller can obviously improve vehicle’s driving smoothness and handling stability, so it meets the applying requirements.

scholarly journals Car drivers coping with hazardous events in real versus simulated situations: Declarative, behavioral and physiological data used to assess drivers’ feeling of presence

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247373
Author(s):  
Elise Gemonet ◽  
Clément Bougard ◽  
Stéphane Masfrand ◽  
Vincent Honnet ◽  
Daniel R. Mestre
Keyword(s):  
Heart Rate ◽  
Physiological State ◽  
Physiological Parameters ◽  
Physiological Data ◽  
High Heart Rate ◽  
The Real ◽  
The Road ◽  
Hazardous Event ◽  
On The Road ◽  
Real Vehicle

More than 1.3 million people lose their lives every year in traffic accidents. Improving road safety requires designing better vehicles and investigating drivers’ abilities more closely. Driving simulators are constantly being used for this purpose, but the question which often arises as to their validity tends to be a barrier to developments in this field. Here we studied the validity of a simulator, defined as how closely users’ behavior under simulated conditions resembles their behavior on the road, based on the concept of drivers’ feeling of presence. For this purpose, the driving behavior, physiological state and declarative data of 41 drivers were tested in the Sherpa2 simulator and in a real vehicle on a track while driving at a constant speed. During each trial, drivers had to cope with an unexpected hazardous event (a one-meter diameter gym ball crossing the road right in front of the vehicle), which occurred twice. During the speed-maintenance task, the simulator showed absolute validity, in terms of the driving and physiological parameters recorded. During the first hazardous event, the physiological parameters showed that the level of arousal (Low Heart Rate/High Heart Rate ratio x10) increased up to the end of the drive. On the other hand, the drivers’ behavioral (braking) responses were 20% more frequent in the simulator than in the real vehicle, and the physiological state parameters showed that stress reactions occurred only in the real vehicle (+5 beats per minute, +2 breaths per minute and the phasic skin conductance increased by 2). In the subjects’ declarative data, several feeling of presence sub-scales were lower under simulated conditions. These results suggest that the validity of motion based simulators for testing drivers coping with hazards needs to be questioned.

scholarly journals A Procedure for the Derivation of a Schedule-Based Energy-Equivalent Driving Cycle for Urban Buses

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 518
Author(s):  
Carlo Villante
Keyword(s):  
Performance Prediction ◽  
Driving Cycle ◽  
Vehicle Speed ◽  
Propulsion Systems ◽  
Usual Practice ◽  
Energy Characteristics ◽  
Energy Equivalent ◽  
The Real ◽  
Driving Cycles ◽  
Real Vehicle

The present paper takes the lead from the long-lasting experience gained by the author on mathematical modeling of vehicle energy performances and on the calibration of those models based on real data acquired on buses in real operation. In particular, the paper focuses on a possible way to define a schedule-based energy-equivalent driving cycle which may have a number of applications (e.g., for buses’ performance prediction, propulsion systems choice and net optimization). Specific attention was given to energy-equivalency of the proposed driving cycles to the foreseeable real mission of the vehicles on their scheduled duty (e.g., mean inertial energy on play during vehicle Start and Stops, mean stops in between the arcs, mean vehicle speed and acceleration, etc.): The objective was in fact that of reproducing the same energy characteristics of the real vehicle mission in a simplified way. To this aim, the main energy characteristics of the expected mission were foreseen through a regressive interpolation of data coming from an extensive analysis of onboard measured data, based on independent variables (mean vehicle cruise speed and slope), which could be efficiently estimated by vehicle schedule. There could be a number of possible uses of the so-defined driving cycles (e.g., for buses’ performance prediction, propulsion systems choice and net optimization): All these applications will benefit from the precision of the cycles in predicting energy characteristics of the real vehicle use and will therefore be much more reliable than in usual practice (which normally makes reference to standardized bus cycles with very limited connection to expected vehicle use).

Road-Vehicles Control Logic Integrating Real Time Multibody Model Follower

2015 ◽  
Author(s):  
Isabel Ramirez Ruiz ◽  
Edoardo Sabbioni ◽  
Francesco Braghin ◽  
Federico Cheli
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Vehicle Model ◽  
Control Logic ◽  
Active Systems ◽  
The Real ◽  
Multibody Model ◽  
Drive Control ◽  
Primary Model ◽  
Real Vehicle

The challenge to enhance the vehicle driving and handling with a state estimation and prediction system is presented by fusing a primary real time multibody vehicle model capable of providing a good indication of vehicle stability and control, and a secondary model able to estimate the vehicle state from vehicle real and virtual sensors to correct the indications of the primary model. A mathematical algorithm combines these two models in the drive control system improving the behavior of the active systems of the vehicle. A Multibody vehicle model has been used to achieve a high fidelity simulation of vehicle dynamics. The selected software is LMS.Virtual.Lab Motion with Real-Time Solver which complements the AMESim Real-Time Solver to handle complex real-time 3D-1D mechatronic systems without any simplified conceptual models. A Sensor Signal Processing Model has been developed to estimate the vehicle states and calculating tire-road contact forces and vehicle sideslip angle. The methodological approach uses the equations of motion of the chassis applying the fundamental principles of classical physics: Newtonian method and Euler angles. The control logic is based on the continuous updating of the preview multibody vehicle model by the controller sensors information network, which makes the model forecast behavior closer to the real one and improve comfort and linearity of the vehicle response. The driver inputs (throttle, steer angle and torque, brake, gear) are the same for the MBS real time model and for the real vehicle. A first training logic updates the MBS model based on the real vehicle behavior calculated by the sensor network, where the logic has to update in the MBS model just the factors depending on the vehicle itself (for example car weight, tire temperature, shock absorber damping forces, tires characteristics) and to understand and keep into account different environment variation (wet / dry surface). If the real vehicle is equipped with active control systems to improve handling and stability, as active camber control, drive by wire, ESP, Body movement active controls, the real time multibody model will interact with the models 1D or 3D of these vehicle dynamics controls and will improve their performance with a very high accuracy prediction of their influence on vehicle dynamic response. In conclusion with the help of the preview multibody vehicle model the drive control logic will increase the performance and drive ability of the vehicle with smart logic interacting with all the active systems.

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