A hybrid liar/radar-based deep learning and vehicle recognition engine for autonomous vehicle precrash control
DOI:
https://doi.org/10.15587/1729-4061.2018.141298Keywords:
Deep Learning, LiDAR sensor Dataset, KD Tree Algorithms, Point Cloud, Vehicle Recognition ModuleAbstract
PreCrash problem of Intelligent Control of autonomous vehicles robot is a very complex problem, especially vehicle pre-crash scenarios and at points of intersections in real-time environments.
The goal of this research is to develop a new artificial intelligent adaptive controller for autonomous vehicle Pre-Crash system along with vehicle recognition module and tested in MATLAB including some detailed modules. Following tasks were set: finding Objects in sensor Data (LiDAR. RADAR), Speed and Steering control, vehicle Recognition using convolution neural network and Alexnet.
In this research paper, we implemented a real-time image/Lidar processing. At the beginning, we presented a real-time system which is composed of comprehensive modules, these modules are 3d object detection, object clustering and search, ground removal, deep learning using convolutional neural networks. Starting with nearest vehicle module our target is to find the nearest ahead car and consider it as our primary obstacle.
This paper presents an Adaptive cruise pre-crash system and vehicle recognition. The Adaptive cruise pre-crash system module depends on Deep Learning and LiDAR sensor data, which meant to control the driver reckless behavior on the road by adjusting the vehicle speed to maintain a safe distance from objects ahead (such as cars, humans, bicycle or whatever the object) when the driver tries to raise speed. At the very moment the vehicle recognition module, detects and recognizes the vehicles surrounding to the car.
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Copyright (c) 2018 Bassant Mohamed Elbagoury, Abdel Badeeh Mohamed M. Salem, Rytis Maskeliunas
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