Intention Prediction in Vehicular Environments

Intention Prediction in Vehicular Environments


Title: Intention Prediction in Vehicular Environments
Author: Mitch Mathieu
Course: CISC 499

Description: Teaching machines to drive on their own is an ever-evolving task that requires computer algorithms to understand increasingly complex driving situations. Specifically, the ability to predict the near-future actions of other drivers on the road would help prevent our robotic drivers from unnecessary accidents and create a safer driving environment.

For this research project, I used LiDAR and camera data from autonomous vehicles to detect other cars in three-dimensional space and then predict their future positions. The object detection was done using a convolutional neural network trained to detect cars from 3D LiDAR point clouds which produced a 3D bounding box for each car. The future position of each car was predicted by applying a regression model to the current and previous positions of each bounding box.

View Poster (PDF)

Doug Martin

Systems Specialist, School of Computing, Queen's University