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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...
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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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Deep Structured Reactive Planning
An intelligent agent operating in the real-world must balance achieving ...
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Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
In this paper, we propose an end-to-end self-driving network featuring a...
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Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Self-driving vehicles plan around both static and dynamic objects, apply...
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MultiNet: Multiclass Multistage Multimodal Motion Prediction
One of the critical pieces of the self-driving puzzle is understanding t...
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Safe Motion Planning for Autonomous Driving using an Adversarial Road Model
This paper presents a game-theoretic path-following formulation where th...
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DSDNet: Deep Structured self-Driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop a deep structured energy based model which considers the interactions between actors and produces socially consistent multimodal future predictions. Furthermore, DSDNet explicitly exploits the predicted future distributions of actors to plan a safe maneuver by using a structured planning cost. Our sample-based formulation allows us to overcome the difficulty in probabilistic inference of continuous random variables. Experiments on a number of large-scale self driving datasets demonstrate that our model significantly outperforms the state-of-the-art.
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