Toronto Intelligent Systems Lab
Toronto Intelligent Systems Lab
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Latest Publications
Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation
Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
A Deep Concept Graph Network for Interaction-Aware Trajectory Prediction
Learning Interactive Driving Policies via Data-driven Simulation
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VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
Learning An Explainable Trajectory Generator Using The Automaton Generative Network (AGN)
Vehicle Trajectory Prediction Using Generative Adversarial Network with Temporal Logic Syntax Tree Features
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Differentiable Logic Layer for Rule Guided Trajectory Prediction
Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
Deep Orientation Uncertainty Learning based on a Bingham Loss
Learning Robust Control Policies for End-to-End Autonomous Driving from Data-Driven Simulation
Infrastructure-free NLoS Obstacle Detection for Autonomous Cars
Range-based Cooperative Localization with Nonlinear Observability Analysis
Probabilistic Risk Metrics for Navigating Occluded Intersections
ShadowCam: Real-Time Detection of Moving Obstacles behind a Corner for Autonomous Vehicles
Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees
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