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The Importance of Prior Knowledge in Precise Multimodal Prediction
Roads have well defined geometries, topologies, and traffic rules. While...
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TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
Simulation has the potential to massively scale evaluation of self-drivi...
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Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately p...
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IntentNet: Learning to Predict Intention from Raw Sensor Data
In order to plan a safe maneuver, self-driving vehicles need to understa...
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Diverse Complexity Measures for Dataset Curation in Self-driving
Modern self-driving autonomy systems heavily rely on deep learning. As a...
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MP3: A Unified Model to Map, Perceive, Predict and Plan
High-definition maps (HD maps) are a key component of most modern self-d...
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INFER: INtermediate representations for FuturE pRediction
In urban driving scenarios, forecasting future trajectories of surroundi...
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LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Self-driving vehicles need to anticipate a diverse set of future traffic scenarios in order to safely share the road with other traffic participants that may exhibit rare but dangerous driving. In this paper, we present LookOut, an approach to jointly perceive the environment and predict a diverse set of futures from sensor data, estimate their probability, and optimize a contingency plan over these diverse future realizations. In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that allows us to cover a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models. Unlike previous work in diverse motion forecasting, our diversity objective explicitly rewards sampling future scenarios that require distinct reactions from the self-driving vehicle for improved safety. Our contingency planner then finds comfortable trajectories that ensure safe reactions to a wide range of future scenarios. Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and more comfortable motion plans in long-term closed-loop simulations than current state-of-the-art models.
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