Explore self-driving simulation research and practical testing methodologies
In-depth guides from Winnipeg research centers and beyond
Learn how deep Q-networks train autonomous agents to make real-time driving decisions in complex traffic scenarios.
Understand how multiple vehicles coordinate using multi-agent RL to optimize traffic flow and safety in dense urban environments.
Explore validation methodologies and safety benchmarks used to certify autonomous vehicles before real-world deployment.
Discover how actor-critic methods and policy gradients enable smooth steering and acceleration decisions in autonomous driving.
A practical look at the development pipeline for autonomous systems
Create realistic simulation environments that mirror real-world driving scenarios. This includes road networks, weather conditions, and traffic patterns that the agent needs to navigate.
Train the RL agent using reward signals that encourage safe, efficient driving. The agent learns through millions of simulation episodes, refining its decision-making policy.
Test the trained agent against unseen scenarios and edge cases. This phase identifies weaknesses and ensures the policy generalizes well beyond the training environment.
Winnipeg researchers are advancing these critical topics
Understanding how deep networks make driving decisions. We're developing methods to interpret what the agent learns and why it chooses specific actions.
Testing vehicle responses to edge cases and adversarial scenarios. This research ensures autonomous systems remain safe even under unexpected or challenging conditions.
Bridging the gap between simulation and real-world performance. Our work focuses on making agents trained in simulation effective when deployed on actual vehicles.
