Smart Autodriver Algorithm for Real-Time Autonomous Vehicle Trajectory Control

Milani, Sina; Khayyam, Hamid; Marzbani, Hormoz; Melek, William; Azad, Nasser L.; Jazar, Reza N.

doi:10.1109/tits.2020.3030236

Cited by 22 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Full expressions of the steady-state responses may be found in [56]. The Autodriver algorithm uses the fact that a vehicle on the road must always rotate about the center of curvature of that road to coincide with the center of rotation of the vehicle.…”

Section: A Autodriver Algorithmmentioning

confidence: 99%

Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

Siddiqi

Milani

Jazar

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Section: A Autodriver Algorithmmentioning

confidence: 99%

Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

Siddiqi

Milani

Jazar

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

“…In [18], Hui et al developed a car-following model considering Inter-Vehicle Communication (IVC) to examine each of the vehicle movement and to better identify and hence, adjust the driving behavior in a two-lane traffic system when an incident occurs on a lane. In [11], the authors developed a path-following algorithm for AVs. The model is based on road geometry data as well as vehicle dynamics.…”

Section: Literature Reviewmentioning

confidence: 99%

“…At present and as it will be discussed in details in the next section, several algorithms have been developed to investigate the car-following behavior of AVs. Most of these studies are based on typical mathematical and control modeling algorithms to ensure smooth car-following such that an autonomous vehicle, defined as the follower, keeps following another vehicle, defined as the leader, while maintaining safety distances [9]- [11]. Recently, few studies have promoted the use of Artificial Intelligence in designing car-following models [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

Masmoudi

Friji

Ghazzai

et al. 2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

Car-following theory has received considerable attention as a core component of Intelligent Transportation Systems. However, its application to the emerging autonomous vehicles (AVs) remains an unexplored research area. AVs are designed to provide convenient and safe driving by avoiding accidents caused by human errors. They require advanced levels of recognition of other drivers' driving-style. With car-following models, AVs can use their built-in technology to understand the environment surrounding them and make real-time decisions to follow other vehicles. In this paper, we design an end-to-end carfollowing framework for AVs using automated object detection and navigation decision modules. The objective is to allow an AV to follow another vehicle based on Red Green Blue Depth (RGB-D) frames. We propose to employ a joint solution involving the You Look Once version 3 (YOLOv3) object detector to identify the leader vehicle and other obstacles and a reinforcement learning (RL) algorithm to navigate the self-driving vehicle. Two RL algorithms, namely Q-learning and Deep Q-learning have been investigated. Simulation results show the convergence of the developed models and investigate their efficiency in following the leader. It is shown that, with video frames only, promising results are achieved and that AVs can adopt a reasonable car-following behavior.

show abstract

“…It requires high technological development in that driver intervention is unnecessary in all driving situations and the vehicle can be driven without a driver. There is an absolute need for image processing [2][3] and AI in order to realize completely autonomous driving [4][5][6][7][8]. These technologies comprise the core of autonomous driving, across all of its stages.…”

Section: Introductionmentioning

confidence: 99%

“…It is not general practice to embody all functions of autonomous driving only on the basis of reinforcement learning. Research has been conducted to create local routes, predict collisions, and brake for autonomous driving vehicles on the basis of reinforcement There is an absolute need for image processing [2,3] and AI in order to realize completely autonomous driving [4][5][6][7][8]. These technologies comprise the core of autonomous driving, across all of its stages.…”

Section: Introductionmentioning

confidence: 99%

A Study on an Enhanced Autonomous Driving Simulation Model Based on Reinforcement Learning Using a Collision Prevention Model

2021

View full text Add to dashboard Cite

This paper set out to revise and improve existing autonomous driving models using reinforcement learning, thus proposing a reinforced autonomous driving prediction model. The paper conducted training for a reinforcement learning model using DQN, a reinforcement learning algorithm. The main aim of this paper was to reduce the time spent on training and improve self-driving performance. Rewards for reinforcement learning agents were developed to mimic human driving behavior as much as possible. High rewards were given for greater distance travelled within lanes and higher speed. Negative rewards were given when a vehicle crossed into other lanes or had a collision. Performance evaluation was carried out in urban environments without pedestrians. The performance test results show that the model with the collision prevention model exhibited faster performance improvement within the same time compared to when the model was not applied. However, vulnerabilities to factors such as pedestrians and vehicles approaching from the side were not addressed, and the lack of stability in the definition of compensation functions and limitations with respect to the excessive use of memory were shown.

show abstract

Smart Autodriver Algorithm for Real-Time Autonomous Vehicle Trajectory Control

Cited by 22 publications

References 27 publications

Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

A Study on an Enhanced Autonomous Driving Simulation Model Based on Reinforcement Learning Using a Collision Prevention Model

Contact Info

Product

Resources

About