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12/21/2022
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality...
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12/16/2022
JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving
We propose JFP, a Joint Future Prediction model that can learn to genera...
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07/12/2022
Wayformer: Motion Forecasting via Simple Efficient Attention Networks
Motion forecasting for autonomous driving is a challenging task because ...
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06/08/2022
Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting
Behavior prediction models have proliferated in recent years, especially...
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11/29/2021
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
Predicting the future behavior of road users is one of the most challeng...
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04/20/2021
Identifying Driver Interactions via Conditional Behavior Prediction
Interactive driving scenarios, such as lane changes, merges and unprotec...
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08/19/2020
TNT: Target-driveN Trajectory Prediction
Predicting the future behavior of moving agents is essential for real wo...
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10/12/2019
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
Predicting human behavior is a difficult and crucial task required for m...
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06/21/2019
Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions
We focus on the problem of predicting future states of entities in compl...
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11/20/2015
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
Current approaches for fine-grained recognition do the following: First,...
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08/06/2012
