Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

The perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information into the autonomous driving process, which are essential for achieving human-like driving in these two methods....

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Bibliographic Details
Published in:IEEE transactions on cognitive and developmental systems 2019-03, Vol.11 (1), p.13-25
Main Authors: Chen, Shitao, Zhang, Songyi, Shang, Jinghao, Chen, Badong, Zheng, Nanning
Format: Article
Language:English
Subjects:
Artificial neural networks
Automotive parts
Autonomous automobiles
Autonomous cars
Autonomous mental development
Autonomous vehicles
Brain
Brain modeling
Cognitive maps
Cognitive models
cognitive robotics
Computer simulation
Data models
Decision making
end-to-end learning
Human behavior
Navigation
Neural networks
Path planning
Planning
Recurrent neural networks
vehicle driving
Visualization
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Summary:The perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information into the autonomous driving process, which are essential for achieving human-like driving in these two methods. In this paper, we propose a novel model for self-driving cars called the brain-inspired cognitive model with attention. This model comprises three parts: 1) a convolutional neural network for simulating the human visual cortex; 2) a cognitive map to describe the relationships between objects in a complex traffic scene; and 3) a recurrent neural network, which is combined with the real-time updated cognitive map to implement the attention mechanism and long-short term memory. An advantage of our model is that it can accurately solve three tasks simultaneously: 1) detecting the free space and boundaries for the current and adjacent lanes; 2) estimating the distances to obstacles and vehicle attitude; and 3) learning the driving behavior and decision-making process of a human driver. Importantly, the proposed model can accept external navigation instructions during an end-to-end driving process. To evaluate the model, we built a large-scale road-vehicle dataset containing over 40 000 labeled road images captured by three cameras placed on our self-driving car. Moreover, human driving activities and vehicle states were recorded at the same time.
ISSN:2379-8920
2379-8939
DOI:10.1109/TCDS.2017.2717451