Fast Scenario Identification and Classification

Self-driving cars represent a transformative innovation in transportation, promising safer and more efficient travel. However, their development faces significant challenges, including accurate prediction, path planning, and safe maneuver execution, especially under varying driving conditions. Ensuring safety across all potential scenarios within the operational design domain is paramount. To effectively address this, we propose developing algorithms that are both cost-effective and low-latency. This would enable faster processing and identification of specific scenarios that are rare or of particular interest to autonomous vehicle developers.

Faculty Supervisor:

Mohamed Shehata

Student:

Partner:

Matt3r Technologies Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

The University of British Columbia - Okanagan

Program: