Back close

Lane Changing and Overtaking Decision Autonomous Vehicles

School: School of Engineering

Lane Changing and Overtaking Decision	 Autonomous Vehicles

In this project we introduce a modified deep auto encoder model based on extreme learning machine (ELM-MDAE) network to capture lane changing and overtaking behaviour of the vehicles and automatically extract the training data in real time. By using extracted data, LightCBM model is trained to establish a novel lane changing and overtaking decision model.

Related Projects

ROS Based Autonomous Shopping Cart with e-Payment Facility
ROS Based Autonomous Shopping Cart with e-Payment Facility
Detection and Prevention of Advanced Persistent Threat (APT) Activitiesin Heterogeneous Networks using SIEM and Deep Learning
Detection and Prevention of Advanced Persistent Threat (APT) Activitiesin Heterogeneous Networks using SIEM and Deep Learning
Development of a Real-time, Process Control Method Based on Neural Network Model Using Feedback of Weld Pool Geometric Parameters Measured by a Vision-based Technique and Experimental Verification for Automated Arc Welding Processes
Development of a Real-time, Process Control Method Based on Neural Network Model Using Feedback of Weld Pool Geometric Parameters Measured by a Vision-based Technique and Experimental Verification for Automated Arc Welding Processes
Object Detection From Cluttered Image
Object Detection From Cluttered Image
Detailed computational modelling and optimization of natural draught cook stoves.
Detailed computational modelling and optimization of natural draught cook stoves.
Admissions Apply Now