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.
How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Reliable prediction of surround vehicle motion is a critical requirement for path planning for autonomous vehicles. In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction. We report our results in terms of maneuver classification accuracy and mean and median absolute error of predicted trajectories against the ground truth for real traffic data collected using vehicle mounted sensors on freeways. An ablative analysis is performed to analyze the relative importance of each cue for trajectory prediction. Additionally, an analysis of execution time for the components of the framework is presented. Finally, we present multiple case studies analyzing the outputs of our model for complex traffic scenarios
fields
cs.RO 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.