Anticipating Autonomous Vehicle Driving based on Multi-Modal Multiple Motion Tasks Network

Recently, research concerning autonomous self-driving vehicles has become very popular. In autonomous vehicles (AVs), different decision-making and learning architectures have been proposed to predict multiple tasks (MTs) or MTs from various datasets or to improve performance among different MTs. In...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & robotic systems 2022-07, Vol.105 (3), Article 69
Main Authors: Khanum, Abida, Lee, Chao-Yang, Hus, Chih-Chung, Yang, Chu-Sing
Format: Article
Language:English
Subjects:
Artificial Intelligence
Autonomous cars
Control
Control tasks
Decision making
Driverless cars
Electrical Engineering
Engineering
Experiments
Lane keeping
Learning
Mechanical Engineering
Mechatronics
Neural networks
Performance enhancement
Performance evaluation
Predictions
Regular Paper
Robotics
Steering
Throttles
Topical collection on ICARSC’20
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, research concerning autonomous self-driving vehicles has become very popular. In autonomous vehicles (AVs), different decision-making and learning architectures have been proposed to predict multiple tasks (MTs) or MTs from various datasets or to improve performance among different MTs. In this paper, a novel and unified multitask learning framework, called Multi-Modal DenseNet ( M 2 -DenseNet), is proposed to predict MTs in a single network in which three long short-term memory units act as the output (MTs). Accordingly, the proposed M 2 -DenseNet can predict three different motion decision-making tasks, i.e., the steering angle, speed, and throttle, to control AV driving. Moreover, M 2 -DenseNet can greatly reduce the time complexity (e.g., to less than 5 ms) because the different prediction tasks can be predicted simultaneously. We conduct comprehensive experiments with the lane-keeping task based on two control mechanisms using the proposed M 2 -DenseNet and other existing methods to evaluate the performance. The experiments demonstrate that M 2 -DenseNet significantly outperforms other state-of-the-art methods with the accuracies of the three control tasks being approximately 98%, 99%, and 98%, respectively. The mean squared error between the predicted value and the ground truth is reported in the experiments, with values for the steering angle, speed, and throttle of 0.0250, 0.0210, and 0.0242, respectively.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-022-01677-2