Current projects

ProcSy dataset sample frame

Real-world, large-scale semantic segmentation datasets are expensive and time-consuming to create. Thus, the research community has explored the use of video game worlds and simulator environments to produce large-scale synthetic datasets, mainly to supplement the real-world ones for training deep neural networks. Another use of synthetic datasets is to enable highly controlled and repeatable experiments, thanks to the ability to manipulate the content and rendering of the synthesized imagery.

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing work creating synthetic data for autonomous driving with GTA V have not released their datasets and rely on an in-game raycasting function which represents people as cylinders and can fail to capture vehicles past 30 metres.

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WiseMove is a modular safe deep reinforcement learning framework for motion planning, combining hierarchical learning and temporal logic constraints.

The project is hosted at git.uwaterloo.ca/wise-lab/wise-move.

Assurance process model

The project objective is to develop methods for assuring the safety of systems that rely on machine learning, such as automated vehicles.

Machine Learning (ML) in Driving Automation

WISE Drive Overview

What is WISE Drive?

WISE Drive is a framework for analyzing and specifying driving behavior requirements on ADS-operated vehicles. Other uses include

Completed projects

Clafer is a lightweight structural and behavioral modeling language.

More more information, visit the new official website: clafer.org

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Projects by status

Opportunities

We are looking for postdocs and graduate students interested in working on all aspects of autonomous driving.

For more information, visit Open positions.