paulyehtw/Multi-Vehicle-Intersection-planning-with-MPC-approach-for-autonomous-vehicles

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Created almost 6 years ago
Updated almost 6 years ago

paulyehtw/Multi-Vehicle-Intersection-planning-with-MPC-approach-for-autonomous-vehicles

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Implement an MPC approach to coordinate multiple automated vehicles with fixed priorities at an unsignalized intersection

Motion-Planning-on-CARLA

Implement Motion Planning for autonomous car on CARLA simulator

Lidar-and-Radar-sensor-fusion-with-Extended-Kalman-Filter

Fusing Lidar and Radar data with Extended Kalman Filter (EKF)

Lane-Keeping-Assist-on-CARLA

Implementing Lane Keeping Assist (LKA) on CARLA simulator

Motion-planning-based-on-Model-Predictive-control-and-Bezier-spline

A quasi Hybrid A* method is introduced for motion planning of autonomous driving car, based on MPC and Bezier spline

Jupyter Notebook

Sensor-Fusion-Using-ES-EKF

Implement Error-State Extended Kalman Filter on fusing data from IMU, Lidar and GNSS.

Kalman-Filter-Based-GPS-Signal-Tracking

Noisy GPS signal filtering algorithm with Kalman Filter

Intersection-planning-with-MPC-approach-for-autonomous-vehicles

Implement an MPC approach to coordinate automated vehicles with fixed priorities

SFND_Radar_Target_Generation_and_Detection

With Radar data, use Fast Fourier Transform(FFT) and 2D CA-CFAR to derive range and velocity of the objects

Machine-Learning

Assignments for Machine Learning course on Coursera from Stanford University

Environment-Perception-For-Self-Driving-Cars

Extract useful scene information from semantic segmentation from neural networks to allow self-driving cars to safely and reliably traverse their environment

Jupyter Notebook