Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

2021 ◽  
Author(s):  
YaZhou Zhou ◽  
RuoChen Wang ◽  
RenKai Ding ◽  
DeHua Shi ◽  
Qing Ye
Keyword(s):  
Change Process ◽  
Hierarchical Control ◽  
Lane Change ◽  
Car Following ◽  
Driving Stability

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving

2018 ◽  
Vol 3 (3) ◽  
pp. 276-286 ◽  
Author(s):  
Yihuan Zhang ◽  
Qin Lin ◽  
Jun Wang ◽  
Sicco Verwer ◽  
John M. Dolan
Keyword(s):  
Autonomous Driving ◽  
Lane Change ◽  
Car Following ◽  
Intention Estimation

Minimum Safety Distances for Emergency Braking Maneuvers in Car-Following Applications

2020 ◽  
Author(s):  
Devin Schafer ◽  
Pingen Chen
Keyword(s):  
Aerodynamic Force ◽  
Lane Change ◽  
Passenger Vehicle ◽  
Lane Changing ◽  
Car Following ◽  
Emergency Braking ◽  
Safety Distance ◽  
Bicycle Model ◽  
Vehicle Efficiency ◽  
Different Types

Abstract Platooning/car following has been considered as a promising approach for improving vehicle efficiency due to the reduction of aerodynamic force when closely following a pilot vehicle. However, safety is a major concern in the close car platooning/following. This paper investigates the minimum inter-vehicle distances required for a passenger vehicle to safely travel behind a heavy-duty truck with three different types of emergency maneuvers. The three emergency maneuvers considered are braking only, steering only, and braking then steering, where steering refers to a single lane change maneuver. Numerical analysis is conducted for deriving the clearance space in the braking only scenario. In addition, simulations are conducted in MATLAB/Simulink, using a bicycle model for the vehicle dynamics, to examine the minimum safe following distance for the other two scenarios. The simulation results show that, for initial vehicle speeds greater than 8 m/s, a lane change maneuver requires the shortest safety distance. Braking followed by lane changing usually requires the largest minimum safety distance.

Study on Fuzzy PI Control of Vehicle Yaw Moment Based on Optimal Allocation of Braking Forces

2014 ◽  
Vol 556-562 ◽  
pp. 2293-2296
Author(s):  
Gang Li ◽  
Hai Lan Han ◽  
Chao Wang ◽  
Gao Feng Ma
Keyword(s):  
Optimal Allocation ◽  
Control Method ◽  
Hierarchical Control ◽  
Pi Control ◽  
Direct Yaw Moment Control ◽  
Yaw Moment Control ◽  
Brake Force ◽  
Moment Control ◽  
Driving Stability ◽  
Yaw Moment

For vehicle direct yaw moment control (DYC) ,the additional yaw moment decision method based on the fuzzy PI control and optimal allocation method of yaw moment based on quadratic programming are studied. Yaw moment control adopts hierarchical control method.The fuzzy PI controller and brake force optimization distributor are designed. The control method is verified through the Matlab/Simulink and CarSim co-simulation experiment.The results show that the control method can make the vehicle track the expected value better and improve the driving stability effectively.

Decision Analysis of Lane Change Based on Fuzzy Logic

2013 ◽  
Vol 419 ◽  
pp. 790-794 ◽  
Author(s):  
Wen Shi ◽  
Ya Ping Zhang
Keyword(s):  
Fuzzy Logic ◽  
Change Process ◽  
Road Surface ◽  
Driver Model ◽  
Steering Wheel ◽  
Surface Model ◽  
Lane Change ◽  
The Road ◽  
Fuzzy Logic Analysis ◽  
Test Result

Aiming at the complexity of lane change process, fuzzy logic analysis method was proposed to analyzing this behavior. By assaying the multi lane change scene that the drivers may choose, influencing factors were quantified. Each indicator factor after quantified was treated as model input. PID models of driver, vehicle and road surface were established in Simulink condition. The road surface model controls whether the lane change process will be conducted, and the driver model will export angle of steering wheel to deciding the efficiency of lane change process. Real road test was conducted and the test result shows that information between human and vehicle can be fused sufficiently.

Lane Change Trajectory Planning and Simulation for Intelligent Vehicle

2013 ◽  
Vol 671-674 ◽  
pp. 2843-2846 ◽  
Author(s):  
Chang Wang ◽  
Chu Qing Zheng
Keyword(s):  
Trajectory Planning ◽  
Change Process ◽  
Intelligent Vehicle ◽  
Planning Problem ◽  
Lane Change ◽  
Change Model ◽  
Change Trajectories ◽  
Basic Model ◽  
Duration Time

Aiming at the trajectory planning problem of intelligent vehicle during lane change process, 7 polynomials lane change model was used to control vehicle. Basic model of this model was established at first, and then lane change trajectories were solved by using restriction of movement state. At last, the commonly form of lane change trajectories were obtained. Using real road duration time of lane change, lane change trajectories were simulated with MATLAB. The results shows that this model was suitable for lane change trajectories planning in different speed and it can be used for intelligent vehicle controlling.

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