Driving on Highway by Using Reinforcement Learning with CNN and LSTM Networks

Szoke, Laszlo; Aradi, Szilárd; Bécsi, Tamás; Gáspár, Péter

doi:10.1109/ines49302.2020.9147185

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Observation and measurement took place every ten episodes. Unlike previous studies [2,3], a certain range of 0~3 was shown for the average compensation values. This is because existing reinforcement learning-based autonomous The driving time for each episode without the application of the collision prevention algorithm was examined.…”

Section: Evaluation Of Autonomus Driving Efficiency Using Reinforceme...contrasting

confidence: 78%

“…The hidden layer consisted of three CNN layers and flatten layers. Convolutional layers of [8,8,16], [4,4,32], and [3,3,32] are used for each of the three CNN layers, respectively, and ReLu functions are used for active functions. After altering the data in one dimension with the flatten layers, training is carried out to determine six behaviors through the dense layers.…”

Section: Design Of the Reward Functionmentioning

confidence: 99%

“…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%

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

2021

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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.

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Section: Evaluation Of Autonomus Driving Efficiency Using Reinforceme...contrasting

confidence: 78%

Section: Design Of the Reward Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…In [3], Liu et al applied double deep Q-network (DDQN) to enhance the driving safety and fuel consumption of AVs. In [4], Szőke et al applied deep Q-learning to control a vehicle in a variety of environments. The reward was based on the absolute difference between the current and desired velocity.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies

Yan¹,

Tabassum²

2022

Preprint

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Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs). However, the uncertainties in the road-traffic and AVs' wireless connections can severely impair timely decisionmaking. It is thus critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions while maximizing the communication data rates. In this paper, we develop a reinforcement learning (RL) framework to characterize efficient network selection and autonomous driving policies in a multi-band vehicular network (VNet) operating on conventional sub-6GHz spectrum and Terahertz (THz) frequencies. The proposed framework is designed to (i) maximize the traffic flow and minimize collisions by controlling the vehicle's motion dynamics (i.e., speed and acceleration) from autonomous driving perspective, and (ii) maximize the data rates and minimize handoffs by jointly controlling the vehicle's motion dynamics and network selection from telecommunication perspective. We cast this problem as a Markov Decision Process (MDP) and develop a deep Q-learning based solution to optimize the actions such as acceleration, deceleration, lane-changes, and AV-base station assignments for a given AV's state. The AV's state is defined based on the velocities and communication channel states of AVs. Numerical results demonstrate interesting insights related to the inter-dependency of vehicle's motion dynamics, handoffs, and the communication data rate. The proposed policies enable AVs to adopt safe driving behaviors with improved connectivity.

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Leveraging semantic similarity to mitigate the severity of misclassification for safety critical applications

Sebastian

2022

Multimed Tools Appl

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Driving on Highway by Using Reinforcement Learning with CNN and LSTM Networks

Cited by 8 publications

References 13 publications

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

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

Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies

Leveraging semantic similarity to mitigate the severity of misclassification for safety critical applications

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