Proof and experiments show the MoN loss approximates the square root of the ground truth PDF in probabilistic trajectory prediction instead of the PDF.
Convolutional Social Pooling for Vehicle Trajectory Prediction
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic context, i.e., the motion and relative spatial configuration of neighboring vehicles. In this paper we propose an LSTM encoder-decoder model that uses convolutional social pooling as an improvement to social pooling layers for robustly learning interdependencies in vehicle motion. Additionally, our model outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. We evaluate our model using the publicly available NGSIM US-101 and I-80 datasets. Our results show improvement over the state of the art in terms of RMS values of prediction error and negative log-likelihoods of true future trajectories under the model's predictive distribution. We also present a qualitative analysis of the model's predicted distributions for various traffic scenarios.
years
2019 3verdicts
UNVERDICTED 3representative citing papers
A hybrid learning-plus-planning architecture for predicting vehicle trajectories that handles both rational and irrational human behaviors and remains stable on unseen scenarios.
RobustTP uses a non-linear motion model plus instance segmentation to create noisy trajectories, then an LSTM-CNN to predict 5-second future positions of heterogeneous agents in dense traffic, claiming up to 18% ADE and 35.5% FDE gains over prior methods.
citing papers explorer
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Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction
Proof and experiments show the MoN loss approximates the square root of the ground truth PDF in probabilistic trajectory prediction instead of the PDF.
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Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors
A hybrid learning-plus-planning architecture for predicting vehicle trajectories that handles both rational and irrational human behaviors and remains stable on unseen scenarios.
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RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs
RobustTP uses a non-linear motion model plus instance segmentation to create noisy trajectories, then an LSTM-CNN to predict 5-second future positions of heterogeneous agents in dense traffic, claiming up to 18% ADE and 35.5% FDE gains over prior methods.