Algolux Brings Technology Expertise to Help Self-Driving Cars Handle Winter Road Conditions
WinTOR’s on its way: U of T partnership to train self-driving cars to handle tough winter conditions
Algolux is proud to be a part of “WinTOR: Autonomous Driving in Adverse Conditions”, alongside General Motors, LG Electronics, and Applanix.
As Steven Lake Waslander, Associate Professor at the University of Toronto Institute for Aerospace Studies puts it, “Reduced visibility limits perception performance, and slippery road surfaces are a big challenge for vehicle control.”
“Algolux’s mission is to solve the issue of computer vision robustness in harsh driving conditions, a fundamental problem not effectively addressed by current approaches,” says Felix Heide, co-founder and chief technology officer of Algolux. “As a Canadian company, we are thrilled to bring our expertise to this project and continue to advance the state-of-the-art in perception technologies.”
The WinTOR project is divided into three broad themes:
- Sensor filtering for object detection: New ways of analyzing the data from sensors such as visual cameras, radar and lidar will help to separate the signals that represent real objects from the noise caused by falling or blowing snow. Strategies will include both pre-processing techniques and improved artificial intelligence algorithms trained to be aware of the limits of their own performance.
- Sensor fusion, localization and tracking: While today’s self-driving cars can reliably determine where they are in relation to their surroundings, the techniques they use begin to break down under adverse driving conditions. The team will leverage new algorithmic strategies in vision and lidar registration, as well as new sensing options, such as ground-penetrating and automotive radar, to make localization algorithms more resilient in adverse conditions.
- Prediction, planning and control: self-driving cars of the future will need to change the way they drive in response to winter hazards. For example, they might take a slightly different path to avoid a snowdrift or slow down when driving over a section of road that their sensors have perceived as particularly slippery. They will learn the implications of adverse weather on the vehicles around them and be able to assess the increased uncertainty of outcomes, enabling them to plan actions that can be executed reliably in winter conditions.