MATLAB sample codes for mobile robot navigation

Configures the FMCW waveform based on the system requirements. Then defines the range and velocity of a target and simulates its displacement. For the same simulation loop process, the transmit and receive signals are computed to determine the *beat* signal. Then it performs a Range FFT on the received signal to determine the Range Towards the e…

Autonomous Vehicle modelling using MATLAB and Simulink

Certifiable Outlier-Robust Geometric Perception

Implementation of implicit dual control-based active uncertainty learning for human-robot interaction - WAFR 2022 & IJRR 2023

Implementation of SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction - RAL 2022

This GitHub repository implements two Model Predictive Control (MPC) approaches for active front steering systems in autonomous vehicles. The methods are inspired by groundbreaking scientific papers. The project utilizes MATLAB and Simulink, requiring

Lane detection MATLAB code for Kalman Filter book chapter: Lane Detection

Generating targets and detecting range and velocity from simulated FMCW waveform radar signals using the Range/Doppler FFT method and displaying targets using the 2D CFAR visualization.

Spatial-CNN for lane detection in MATLAB.

Recognize traffic sign using Histogram of Oriented Gradients (HOG) and Colorspace based features. Support Vector Machines (SVM) is used for classifying images.

Using deep learning to predict the motion of a MPC-controlled vehicle

MATLAB sample codes for Robotics engineering

Tool to design and optimize autonomous vehicle concepts.

Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture

This project uses Recurrent Neural Networks (RNNs) to classify the behavior of objects in traffic based on their risk level, enhancing the safety of autonomous vehicles. Developed as part of the MATLAB-Simulink Challenge by MathWorks.

In this (ring-road multi-lane) traffic simulation, one can take the wheel of a car for changing lanes, acceleration and deceleration in real time, also creating congestion. One can observe the traffic dynamics and stop-and-go waves through plotting the time-space diagram of trajectories

This is an autonomous vehicle's planning algorithm. This algorithm is based on APOLLO emplanner, based on sampling.

Traditional IMM Filter vs. Unsupervised KalmanNet. A MATLAB-based comparison for denoising GPS data using classical state estimation and deep learning with Negative Log-Likelihood (NLL) loss.