Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking

Author:

Hasanujjaman Muhammad1ORCID,Chowdhury Mostafa Zaman1ORCID,Jang Yeong Min2ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh

2. Department of Electronics Engineering, Kookmin University, Seoul 02707, Republic of Korea

Abstract

Complete autonomous systems such as self-driving cars to ensure the high reliability and safety of humans need the most efficient combination of four-dimensional (4D) detection, exact localization, and artificial intelligent (AI) networking to establish a fully automated smart transportation system. At present, multiple integrated sensors such as light detection and ranging (LiDAR), radio detection and ranging (RADAR), and car cameras are frequently used for object detection and localization in the conventional autonomous transportation system. Moreover, the global positioning system (GPS) is used for the positioning of autonomous vehicles (AV). These individual systems’ detection, localization, and positioning efficiency are insufficient for AV systems. In addition, they do not have any reliable networking system for self-driving cars carrying us and goods on the road. Although the sensor fusion technology of car sensors came up with good efficiency for detection and location, the proposed convolutional neural networking approach will assist to achieve a higher accuracy of 4D detection, precise localization, and real-time positioning. Moreover, this work will establish a strong AI network for AV far monitoring and data transmission systems. The proposed networking system efficiency remains the same on under-sky highways as well in various tunnel roads where GPS does not work properly. For the first time, modified traffic surveillance cameras have been exploited in this conceptual paper as an external image source for AV and anchor sensing nodes to complete AI networking transportation systems. This work approaches a model that solves AVs’ fundamental detection, localization, positioning, and networking challenges with advanced image processing, sensor fusion, feathers matching, and AI networking technology. This paper also provides an experienced AI driver concept for a smart transportation system with deep learning technology.

Funder

Ministry of Science and ICT

National Research Foundation of Korea

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference71 articles.

1. World Health Organization (2023, January 05). Global Status Report on Road Safety. Available online: https://www.who.int/publications/i/item/9789241565684.

2. The global macroeconomic burden of road injuries: Estimates and projections for 166 countries;Chen;Lancet Planet. Health,2019

3. (2023, January 05). National Highway Traffic Safety Administration, Available online: https://crashstats.nhtsa.dot.gov/Api/Public/Publication/812318.

4. (2023, January 05). Waymo. Available online: https://waymo.com/company/.

5. (2023, January 06). Tesla. Available online: https://www.tesla.com/autopilot.

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