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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...
01/18/2021 ∙ by Sergio Casas, et al. ∙
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Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
Over the last few years, we have witnessed tremendous progress on many s...
01/17/2021 ∙ by John Phillips, et al. ∙
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End-to-end Interpretable Neural Motion Planner
In this paper, we propose a neural motion planner (NMP) for learning to ...
01/17/2021 ∙ by Wenyuan Zeng, et al. ∙
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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...
01/16/2021 ∙ by Abbas Sadat, et al. ∙
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AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
As self-driving systems become better, simulating scenarios where the au...
01/16/2021 ∙ by Jingkang Wang, et al. ∙
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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...
01/16/2021 ∙ by Alexander Cui, et al. ∙
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Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
In this paper, we tackle the problem of spatio-temporal tagging of self-...
11/12/2020 ∙ by Sean Segal, et al. ∙
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Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
We present a novel method for testing the safety of self-driving vehicle...
08/13/2020 ∙ by Kelvin Wong, et al. ∙
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Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
In this paper we propose a novel end-to-end learnable network that perfo...
08/13/2020 ∙ by Abbas Sadat, et al. ∙
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Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
The motion planners used in self-driving vehicles need to generate traje...
10/10/2019 ∙ by Abbas Sadat, et al. ∙
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