Analysis of cooperative driving operation of a car by two humans for the application to automatic driving system

2018 ◽  
Vol 2018 (0) ◽  
pp. 2P1-L16
Author(s):  
Yota HASHIBA ◽  
Ryojun IKEURA ◽  
Tomoya HATTORI ◽  
Soichiro HAYAKAWA ◽  
Shigeyoshi TSUTSUMI ◽  
...  
Keyword(s):  
Driving System ◽  
Automatic Driving ◽  
Cooperative Driving

Level-wise driving knowledge induction for embedded automatic train driving system

2021 ◽  
pp. 107754632110033
Author(s):  
Gang Xiao ◽  
Qinwen Yang ◽  
Fan Yang ◽  
Tao Liu ◽  
Tao Li ◽  
...  
Keyword(s):  
Embedded System ◽  
Fuzzy Rule ◽  
Operating Efficiency ◽  
Driving Experience ◽  
Driving System ◽  
Automatic Driving ◽  
Energy Consumption Reduction ◽  
Experience Levels ◽  
Consumption Reduction ◽  
Regression Learning

Automatic driving of trains can significantly reduce the energy cost and enhance the operating efficiency and safety. The automatic train driving system has to be an embedded system that can run onboard with low power, which necessitates an efficient inference model. In this article, a level-wise driving knowledge induction approach is proposed for embedded automatic train driving systems. The coincident driving patterns in the records of drivers with different experience levels suggest the suitability of a driving experience knowledge rule induction approach. We design a two-level learning approach to obtain both the driving experience pattern in fuzzy rule-based knowledge form and the detailed parameters of velocity and gear by regression learning methods. With 8.93% energy consumption reduction compared with average human drivers, the experiments indicate the effectiveness of our approach.

Hydraulic Pressure Control and Parameter Optimization of Brake-by-Wire System Based on Driver-Automation Cooperative Driving

2018 ◽  
Author(s):  
Xiaohui Liu ◽  
Liangyao Yu ◽  
Sheng Zheng ◽  
Jinghu Chang ◽  
Fei Li
Keyword(s):  
Parameter Optimization ◽  
Pressure Control ◽  
Intelligent Vehicle ◽  
Hydraulic Pressure ◽  
Braking Performance ◽  
Driving Mode ◽  
Automatic Driving ◽  
Cooperative Driving ◽  
Braking System ◽  
Wire System

The automatic driving technology of vehicle is being carried out in real road environment, however, the application of unmanned vehicle still needs proof and practice. Autonomous vehicles will be in the stage of co-drive for a long time, that is, driver-control and autonomous system assisting or autonomous system control and driver assisting. The braking system of the intelligent vehicle needs to work in driver driving mode or automatic driving mode during a long stage. Brake-by-Wire system is the development trend of vehicle braking system. The brake modes of the Brake-by-Wire system can be switched easily and it can satisfy the demand for braking system of the intelligent vehicle. However, when the driving mode changes, the characteristic of the braking intention and braking demand will change. In order to improve the braking performance of the intelligent vehicle, hydraulic pressure control and parameter optimization of the Brake-by-Wire system during different driving modes should be different. Researches are made on hydraulic pressure control and parameter optimization of the Brake-by-Wire system with consideration on differences of braking intensity input and braking requirement between driver driving mode and automatic driving mode through theory analysis, Matlab/Simulink-AMESim simulation and bench test. The study is helpful for improving the braking performance of Brake-by-Wire system in hydraulic pressure control of driver-automation cooperative driving.

An extended optimal velocity difference model in a cooperative driving system

2015 ◽  
Vol 26 (05) ◽  
pp. 1550054
Author(s):  
Jinliang Cao ◽  
Zhongke Shi ◽  
Jie Zhou
Keyword(s):  
Traffic Flow ◽  
Linear Stability Theory ◽  
Difference Model ◽  
Optimal Velocity ◽  
Driving System ◽  
Cooperative Driving ◽  
Proposed Model ◽  
De Vries ◽  
The Stability ◽  
Theoretical Results

An extended optimal velocity (OV) difference model is proposed in a cooperative driving system by considering multiple OV differences. The stability condition of the proposed model is obtained by applying the linear stability theory. The results show that the increase in number of cars that precede and their OV differences lead to the more stable traffic flow. The Burgers, Korteweg–de Vries (KdV) and modified Korteweg–de Vries (mKdV) equations are derived to describe the density waves in the stable, metastable and unstable regions, respectively. To verify these theoretical results, the numerical simulation is carried out. The theoretical and numerical results show that the stabilization of traffic flow is enhanced by considering multiple OV differences. The traffic jams can be suppressed by taking more information of cars ahead.

scholarly journals Research on Automatic Train Driving Based on Fuzzy Proportion Integration Differentiation Iterative Control

2021 ◽  
Vol 283 ◽  
pp. 02021
Author(s):  
Zhengsheng Qi ◽  
Bohong Liu ◽  
Mengmeng Wang
Keyword(s):  
High Speed ◽  
Convergence Condition ◽  
Vital Role ◽  
Fuzzy Pid ◽  
Parameter Uncertainties ◽  
Efficient Operation ◽  
Transportation Services ◽  
Driving System ◽  
Automatic Driving ◽  
Iterative Control

Automatic train driving system is an important subsystem of train operation control system, which can provide passengers with punctual, accurate, efficient and fast transportation services. At the same time, the accurate stop, comfort and stability of the train is an important index to measure the control performance of the train automatic driving system, and the accurate stop plays a vital role in the efficient operation of the train. Based on the characteristics of high-speed train parking, an accurate parking algorithm based on fuzzy PID iterative control was proposed to solve the problem of low parking accuracy caused by frequent switching of control output. On the basis of solving the differential equation of the train braking model, the gradient of the system is obtained, and then the learning parameters of the convergence condition are obtained to overcome the repeated uncertainty in the stopping stage. The simulation results show that the fuzzy PID iterative control for asymptotic stability is an effective method to realize the precise parking of trains, and has strong robustness against the train parameter uncertainties and external disturbances.

scholarly journals Influences of Single-Lane Automatic Driving Systems on Traffic Efficiency and CO2 Emissions on China’s Motorways

2021 ◽  
Vol 11 (22) ◽  
pp. 11032
Author(s):  
Haokun Song ◽  
Fuquan Zhao ◽  
Zongwei Liu
Keyword(s):  
Co2 Emissions ◽  
Penetration Rate ◽  
Connected Vehicles ◽  
Driving System ◽  
Traffic Efficiency ◽  
Automatic Driving ◽  
Driving Behaviors ◽  
Road Capacity ◽  
Long Time ◽  
Carbon Dioxide Co2

There are big differences between the driving behaviors of intelligent connected vehicles (ICVs) and traditional human-driven vehicles (HVs). ICVs will be mixed with HVs on roads for a long time in the future. Different intelligent functions and different driving styles will affect the condition of traffic flow, thereby changing traffic efficiency and emissions. In this paper, we focus on China’s expressways and secondary motorways, and the impacts of the ‘single-lane automatic driving system’ (SLADS) on traffic delay, road capacity and carbon dioxide (CO2) emissions were studied under different ICV penetration rates. Driving styles were regarded as important factors for scenario analysis. We found that with higher volume input, SLADS has an optimizing effect on traffic efficiency and CO2 emissions generally, which will be more significant as the ICV penetration rate increases. Additionally, enhancing the aggressiveness of driving behavior appropriately is an effective way to amplify the benefits of SLADS.

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