Driving Tasks Transfer in Deep Reinforcement Learning for
Decision-making of Autonomous Vehicles
- URL: http://arxiv.org/abs/2009.03268v2
- Date: Sat, 10 Oct 2020 14:16:31 GMT
- Title: Driving Tasks Transfer in Deep Reinforcement Learning for
Decision-making of Autonomous Vehicles
- Authors: Hong Shu, Teng Liu, Xingyu Mu, Dongpu Cao
- Abstract summary: This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments.
The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely.
Decision-making strategies related to similar tasks are transferable.
- Score: 6.578495322360851
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge transfer is a promising concept to achieve real-time
decision-making for autonomous vehicles. This paper constructs a transfer deep
reinforcement learning framework to transform the driving tasks in
inter-section environments. The driving missions at the un-signalized
intersection are cast into a left turn, right turn, and running straight for
automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive
through the intersection situation efficiently and safely. This objective
promotes the studied vehicle to increase its speed and avoid crashing other
vehicles. The decision-making pol-icy learned from one driving task is
transferred and evaluated in another driving mission. Simulation results reveal
that the decision-making strategies related to similar tasks are transferable.
It indicates that the presented control framework could reduce the time
consumption and realize online implementation.
Related papers
- Deep Q-Network Based Decision Making for Autonomous Driving [1.0152838128195467]
This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory.
A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner.
The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers.
arXiv Detail & Related papers (2023-03-21T07:01:22Z) - Generative AI-empowered Simulation for Autonomous Driving in Vehicular
Mixed Reality Metaverses [130.15554653948897]
In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome.
Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly.
We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
arXiv Detail & Related papers (2023-02-16T16:54:10Z) - Prediction Based Decision Making for Autonomous Highway Driving [3.6818636539023175]
This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model.
It considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving.
The results show that the proposed PDRL model improves the decision-making performance compared to a Deep Reinforcement Learning (DRL) model by decreasing collision numbers.
arXiv Detail & Related papers (2022-09-05T19:28:30Z) - Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle.
In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
arXiv Detail & Related papers (2022-07-05T16:33:20Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked
Vehicles [54.61668577827041]
We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving.
Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
arXiv Detail & Related papers (2022-05-04T17:55:12Z) - Parallelized and Randomized Adversarial Imitation Learning for
Safety-Critical Self-Driving Vehicles [11.463476667274051]
It is essential to consider reliable ADAS function coordination to control the driving system, safely.
This paper proposes a randomized adversarial imitation learning (RAIL) algorithm.
The proposed method is able to train the decision maker that deals with the LIDAR data and controls the autonomous driving in multi-lane complex highway environments.
arXiv Detail & Related papers (2021-12-26T23:42:49Z) - Deep Structured Reactive Planning [94.92994828905984]
We propose a novel data-driven, reactive planning objective for self-driving vehicles.
We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
arXiv Detail & Related papers (2021-01-18T01:43:36Z) - A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the
First CARLA Autonomous Driving Challenge [49.976633450740145]
This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment.
Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
arXiv Detail & Related papers (2020-10-23T18:07:48Z) - Decision-making at Unsignalized Intersection for Autonomous Vehicles:
Left-turn Maneuver with Deep Reinforcement Learning [17.715274169051494]
This work proposes a deep reinforcement learning based left-turn decision-making framework at unsignalized intersection for autonomous vehicles.
The presented decision-making strategy could efficaciously reduce the collision rate and improve transport efficiency.
This work also reveals that the constructed left-turn control structure has a great potential to be applied in real-time.
arXiv Detail & Related papers (2020-08-14T22:44:26Z) - Dueling Deep Q Network for Highway Decision Making in Autonomous
Vehicles: A Case Study [9.602219035367066]
This work optimize the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL)
The overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem.
Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.
arXiv Detail & Related papers (2020-07-16T14:09:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.