Published 23-06-2024
Keywords
- Autonomous Vehicles,
- AVs
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
The AV is equipped with various sensors to continuously monitor its immediate surroundings. These sensors are visual, ultrasonic, time-of-flight, light detection and ranging (lidar), and radar sensors. In addition, a global positioning system receiver, an inertial measurement unit, a dead reckoning cluster, and differential global position system correction data are used. Perception and sensing algorithms allow the AV to fully understand and accurately predict its trajectory in the dynamic, complex, and uncertain traffic environment. Map learning is one of the fundamental components of the algorithms, in order to keep the AV within its operational design domain. This paper provides an overview of an end-to-end pipeline for creating accurate surroundings maps for autonomous vehicles using machine-learning techniques. With the aid of deep learning and sophisticated probabilistic models, all of the individual steps in this perception pipeline can be solved in a unified and highly accurate way, including input warping and alignment, sensor enhancement, and characteristics inference. The 2022 study by Shaik and Sadhu focuses on integrating biometrics with blockchain for IAM security.
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