PATH TRACKING FOR AN AUTONOMOUS VEHICLE DURING EMERGENCY CONDITION

An autonomous vehicle's primary function is detecting and tracking the road course precisely and correctly without a driver's assistance. As a result, implementing appropriate controllers is critical for improving the vehicle's stability and movement responsiveness. The performance of adaptive Stanley controlled is evaluated in this paper using numerical simulations. The Stanley controller's most common geometric controller for vehicle path tracking algorithms is compared based on their trajectory tracking analyses on various vehicle speed maneuvers. Stanley calculates steering based on the difference between the vehicle's lateral position and heading angle. The difference between desired coordinates and present coordinates of the vehicle along the path is used to calculate lateral, longitudinal, and vehicle heading orientation angle using the future prediction control technique. The results demonstrate that the Stanley controller outperforms the emergency trajectory with more consistent trajectory tracking and steady-state error.

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Autonomous vehicle path tracking control considering the stability under lane change

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651821991357 ◽

2021 ◽

pp. 095965182199135

Author(s):

Xiaolong Chen ◽

Bing Zhou ◽

Xiaojian Wu

Keyword(s):

Tracking Control ◽

High Speed ◽

Unscented Kalman Filter ◽

Linear Time ◽

Autonomous Vehicle ◽

Path Tracking ◽

Steering Angle ◽

Adhesion Coefficient ◽

The Road ◽

Vehicle Path

Considering that when a vehicle travels on a low friction coefficient road with high speed, the path tracking ability declines. To keep the performance of path tracking and improve the stabilization under that situation, this article presents approaches to estimate the parameters and control the vehicle. First, the key states of the vehicle and the road adhesion coefficient are estimated by the unscented Kalman filter. This is followed by applying the linear time-varying model-based predictive controller to achieve path tracking control, and the initial tire steering angle control rate is obtained. Finally, the steering angle compensation controller is simultaneously designed by a simple receding horizon corrector algorithm to improve vehicle stability when the path is tracked on a low-adhesion coefficient or at high speed. The performance of the proposed approach is evaluated by software CarSim and MATLAB/Simulink. Simulation results show that an improvement in the performance of path tracking and stabilization can be achieved by the integrated controller under the variable road adhesion coefficient condition and high speed with 110 km/h.

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Learning based Predictive Error Estimation and Compensator Design for Autonomous Vehicle Path Tracking

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea48937.2020.9248357 ◽

2020 ◽

Author(s):

Chaoyang Jiang ◽

Hanqing Tian ◽

Jibin Hu ◽

Jiankun Zhai ◽

Chao Wei ◽

...

Keyword(s):

Error Estimation ◽

Autonomous Vehicle ◽

Path Tracking ◽

Predictive Error ◽

Compensator Design ◽

Vehicle Path

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Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

IEEE Access ◽

10.1109/access.2021.3112560 ◽

2021 ◽

Vol 9 ◽

pp. 128233-128249

Author(s):

Mohammad Rokonuzzaman ◽

Navid Mohajer ◽

Saeid Nahavandi ◽

Shady Mohamed

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Vehicle Dynamics ◽

Autonomous Vehicle ◽

Path Tracking ◽

Vehicle Path

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Fuzzy logic controller for autonomous vehicle path tracking

2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) ◽

10.1109/sta.2017.8314969 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sami Allou ◽

Youcef Zennir ◽

Aissa Belmeguenai

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Autonomous Vehicle ◽

Path Tracking ◽

Vehicle Path

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Optimized Potential Radius Reference Generator Algorithm for Autonomous Vehicle Controller Development

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.663.198 ◽

2014 ◽

Vol 663 ◽

pp. 198-202 ◽

Cited By ~ 2

Author(s):

Muhammad Aizzat Zakaria ◽

Hairi Zamzuri ◽

Rosbi Mamat ◽

Saiful Amri Mazlan

Keyword(s):

Steady State ◽

Trajectory Tracking ◽

Autonomous Vehicle ◽

The Road ◽

Reference Generator ◽

Tracking Controller ◽

Simulation And Experiment ◽

The Common ◽

Trajectory Tracking Controller ◽

Reference Input

Trajectory tracking for autonomous vehicle is one of the field that researchers pay attention. The ultimate goal for trajectory tracking is to track the pre-defined path and follow the reference path with zero steady state error. The common modules for trajectory tracking field are reference generator, controller and plant. While most of the researchers are focusing on the controller development, less work has focused on the optimized reference generator. Optimized reference generator ensures the reference input to the controller is the optimized desired points in order to develop a good controller. Therefore, this work presents the reference generator algorithm that select the best point from the road coordinate profile before being send to the controller. The method is using the vehicle potential field and the modification from Dijkstra’s algorithm to generate the path. This algorithm is useful for trajectory tracking controller development. The algorithm is verified using simulation and experiment.

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Manoeuvring speed of a 6×6 autonomous vehicle using a database obtained from multi-body dynamic simulation

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1243/09544070jauto1102 ◽

2009 ◽

Vol 223 (8) ◽

pp. 979-985 ◽

Cited By ~ 9

Author(s):

S-L Cho ◽

K-C Yi ◽

J-H Lee ◽

W-S Yoo

Keyword(s):

Dynamic Simulation ◽

Autonomous Vehicle ◽

Sensor Data ◽

Vehicle Speed ◽

The Road ◽

Driving Speed ◽

Road Roughness ◽

Motion Speed ◽

Vertical Accelerations ◽

Vehicle Dynamic Simulation

For an autonomous vehicle that travels off-road, the driving speed is limited by the driving circumstances. To decide on a stable manoeuvring speed, the driving system should consider road conditions such as the height of an obstacle and road roughness. In general, an autonomous vehicle has many sensors to preview road conditions, and the information gathered by these sensors can be used to find the proper path for the vehicle to avoid unavoidable obstacles. However, sensor data are insufficient for determining the optimal vehicle speed, which could be obtained from the dynamic response of the vehicle. This paper suggests an algorithm that can determine the optimal vehicle speed running over irregular rough terrains such as when travelling off-road. In the determination of the manoeuvring speed, the vehicle dynamic simulation is employed to decide whether the vehicle response is within or beyond the prescribed limits. To determine the manoeuvring speed in real time, the dynamic simulation should be finished much more quickly than the real motion speed of the vehicle. In this paper, the equation of motion of the vehicle is derived in terms of the chassis local coordinates to reduce the simulation time. The velocity transformation technique, which combines the generality of Cartesian coordinates and the efficiency of relative coordinates, was combined with a symbolic computation to enhance further the computational efficiency. First the developed algorithm calculates the level of the previewed road roughness to determine the manoeuvring speed. Then, the maximum stable speed is judged against the database, which already has stored the maximum vertical accelerations as a function of the road roughness and vehicle speed.

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