Authors:
Hesham M. Eraqi
1
;
Youssef Emad Eldin
2
and
Mohamed N. Moustafa
1
Affiliations:
1
The American University in Cairo, Egypt
;
2
Ain Shams University, Egypt
Keyword(s):
Collision Avoidance, Evolutionary Neural Networks, Genetic Algorithm, Lane Keeping.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Robotics and Intelligent Agents
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Collision avoidance systems can play a vital role in reducing the number of accidents and saving human
lives. In this paper, we introduce and validate a novel method for vehicles reactive collision avoidance using
evolutionary neural networks (ENN). A single front-facing rangefinder sensor is the only input required by
our method. The training process and the proposed method analysis and validation are carried out using
simulation. Extensive experiments are conducted to analyse the proposed method and evaluate its
performance. Firstly, we experiment the ability to learn collision avoidance in a static free track. Secondly,
we analyse the effect of the rangefinder sensor resolution on the learning process. Thirdly, we experiment
the ability of a vehicle to individually and simultaneously learn collision avoidance. Finally, we test the
generality of the proposed method. We used a more realistic and powerful simulation environment
(CarMaker), a camera as an alternative input sensor, and
lane keeping as an extra feature to learn. The
results are encouraging; the proposed method successfully allows vehicles to learn collision avoidance in
different scenarios that are unseen during training. It also generalizes well if any of the input sensor, the
simulator, or the task to be learned is changed.
(More)