A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle

Zhang, Xinyu; Zhou, Mo; Liu, Huaping; Hussain, Amir

Abstract

This paper introduces the functional system architecture of the Mengshi intelligent vehicle, winner of the 2018 World Intelligent Driving Challenge (WIDC). Different from traditional smart vehicles, a cognitive module is introduced in the system architecture to realise the transition from perception to decision-making. This is shown to enhance the practical utility of the smart vehicle, enabling safe and robust driving in different scenes. The collaborative work of hardware and software systems is achieved through multi-sensor fusion and artificial intelligence (AI) technologies, including novel use of deep machine learning and context-aware scene analysis to select optimal driving strategies. Experimental results using both robustness tests and road tests confirm that the Mengshi intelligent vehicle is reliable and robust in challenging environments. This paper describes the major components of this cognitively inspired architecture and discusses the results of the 2018 WIDC.

Citation

Zhang, X., Zhou, M., Liu, H., & Hussain, A. (2020). A Cognitively Inspired System Architecture for the Mengshi Cognitive Vehicle. Cognitive Computation, 12(1), 140-149. https://doi.org/10.1007/s12559-019-09692-6

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2019
• Online Publication Date: Nov 15, 2019
• Publication Date: 2020-01
• Deposit Date: Feb 27, 2020
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Electronic ISSN: 1866-9964
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 1
• Pages: 140-149
• DOI: https://doi.org/10.1007/s12559-019-09692-6
• Keywords: Cognitive Neuroscience; Computer Vision and Pattern Recognition; Computer Science Applications
• Public URL: http://researchrepository.napier.ac.uk/Output/2594128