Longitudinal Velocity Control Design with Error Tolerance Strategy for Autonomous Vehicle

2019 ◽  
pp. 1-11
Author(s):  
Umar Zakir Abdul Hamid ◽  
Balaji Ravichandiran ◽  
Murtadha Bazli Tukimat ◽  
Hairi Zamzuri ◽  
Fakhrul Razi Ahmad Zakuan ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Control Design ◽  
Error Tolerance ◽  
Velocity Control ◽  
Tolerance Strategy

Autonomous Vehicle Control Design Framework for Performance and Driveability

2021 ◽  
Author(s):  
Ayhan Arda Araz ◽  
Erhan Ozkaya ◽  
Furkan Kocyigit ◽  
Enes Emre Bulut ◽  
Mertcan Cibooglu ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Control Design ◽  
Vehicle Control ◽  
Design Framework

Understanding the Lateral Dimension of Traffic: Measuring and Modeling Lane Discipline

2021 ◽  
pp. 036119812110318
Author(s):  
Rafael Delpiano
Keyword(s):  
New Technologies ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Lateral Position ◽  
Multiple Cameras ◽  
Lateral Velocity ◽  
Lane Changing ◽  
Lateral Dimension ◽  
Vehicle Position ◽  
Longitudinal Position

There is growing interest in understanding the lateral dimension of traffic. This trend has been motivated by the detection of phenomena unexplained by traditional models and the emergence of new technologies. Previous attempts to address this dimension have focused on lane-changing and non-lane-based traffic. The literature on vehicles keeping their lanes has generally been limited to simple statistics on vehicle position while models assume vehicles stay perfectly centered. Previously the author developed a two-dimensional traffic model aiming to capture such behavior qualitatively. Still pending is a deeper, more accurate comprehension and modeling of the relationships between variables in both axes. The present paper is based on the Next Generation SIMulation (NGSIM) datasets. It was found that lateral position is highly dependent on the longitudinal position, a phenomenon consistent with data capture from multiple cameras. A methodology is proposed to alleviate this problem. It was also discovered that the standard deviation of lateral velocity grows with longitudinal velocity and that the average lateral position varies with longitudinal velocity by up to 8 cm, possibly reflecting greater caution in overtaking. Random walk models were proposed and calibrated to reproduce some of the characteristics measured. It was determined that drivers’ response is much more sensitive to the lateral velocity than to position. These results provide a basis for further advances in understanding the lateral dimension. It is hoped that such comprehension will facilitate the design of autonomous vehicle algorithms that are friendlier to both passengers and the occupants of surrounding vehicles.

scholarly journals Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168060-168074 ◽  
Author(s):  
Wen Yan ◽  
Chunguo Li ◽  
Yongming Huang ◽  
Luxi Yang
Keyword(s):  
Autonomous Vehicles ◽  
Longitudinal Velocity ◽  
Velocity Control

Fastest with No-Overshoot Velocity Control Design of a Two-Differential Robotic Formation

2021 ◽  
pp. 71-79
Author(s):  
Julián-Alejandro Hernández-Gallardo ◽  
Emilio-Jorge González-Galván ◽  
César-Fernando Méndez-Barrios ◽  
Adrian-Josue Guel-Cortez
Keyword(s):  
Control Design ◽  
Velocity Control

scholarly journals Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1079 ◽  
Author(s):  
Fen Lin ◽  
Kaizheng Wang ◽  
Youqun Zhao ◽  
Shaobo Wang
Keyword(s):  
Autonomous Vehicles ◽  
Control Method ◽  
Integrated Control ◽  
Autonomous Vehicle ◽  
Longitudinal Velocity ◽  
Reference Trajectory ◽  
Threshold Method ◽  
Lane Changing ◽  
Information Interaction ◽  
Fitting In

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability.

scholarly journals Simulated Sensor Based Strategies for Obstacle Avoidance Using Velocity Profiling for Autonomous Vehicle FURBOT

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 883 ◽  
Author(s):  
Khayyam Masood ◽  
Rezia Molfino ◽  
Matteo Zoppi
Keyword(s):  
Mathematical Model ◽  
Velocity Profile ◽  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Velocity Control ◽  
Steering Control ◽  
Traffic Lights ◽  
Simulated Environment ◽  
Last Mile ◽  
Robotic Vehicle

Freight Urban Robotic Vehicle (FURBOT) is an autonomous vehicle designed to transport last mile freight to designated urban stations. It is a slow vehicle designed to tackle urban environment with complete autonomy. A slow vehicle may have slightly different strategies for avoiding obstacles. Unlike on a highway, it has to deal with pedestrians, traffic lights and slower vehicles while maintaining smoothness in its drive. To tackle obstacle avoidance for this vehicle, sensor feedback based strategies have been formulated for smooth drive and obstacle avoidance. A full mathematical model for the vehicle is formulated and simulated in MATLAB environment. The mathematical model uses velocity control for obstacle avoidance without steering control. The obstacle avoidance is attained through velocity control and strategies are formulated with velocity profiling. Innovative techniques are formulated in creating the simulated sensory feed-backs of the environment. Using these feed-backs, correct velocity profiling is autonomously created for giving velocity profile input to the velocity controller. Proximity measurements are assumed to be available for the vehicle in its given range of drive. Novelty is attained by manipulating velocity profile without prior knowledge of the environment. Four different type of obstacles are modeled for simulated environment of the vehicle. These obstacles are randomly placed in the path of the vehicle and autonomous velocity profiling is verified in simulated environment. The simulated results obtained show satisfactory velocity profiling for controller input. The current technique helps to tune the existing controller and in designing of a better velocity controller for the autonomous vehicle and bridges the gap between sensor feed-back and controller input. Moreover, accurate input profiling creates less strain on the system and brings smoothness in drive for an overall safer environment.

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