Current projects
Software
WISE Automated Driving System (ADS) is a self-driving car software stack developed by WISE Lab. WISE ADS is a derivative of Autonomoose. It consists of the following components in these three functional areas:
Sensor drivers
Novatel Span Driver (third-party, ported to Python 3, produces GNSS data @ 20 Hz and IMU data @ 100 Hz)
This project involves the development of models of naturalistic human driving behaviour in order to test, validate, and verify behaviour planners of autonomous vehicles
We are implementing a simulator for WISE Automated Driving System (ADS). The simulator is based on UnrealEngine 4.21.
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.
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
Completed projects
TruPercept: Synthetic Data and Trust Modelling for Autonomous Vehicle Cooperative Perception
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.
Abstract. In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datasets. The limited availability of such data is due to the time cost and human effort required to accurately and consistently label real images on a pixel level. Modern sandbox video game engines provide open world environments where traffic and pedestrians behave in a pseudo-realistic manner. This caters well to the collection of a believable road-scene dataset.