58 research outputs found
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
To safely and efficiently navigate through complex traffic scenarios,
autonomous vehicles need to have the ability to predict the future motion of
surrounding vehicles. Multiple interacting agents, the multi-modal nature of
driver behavior, and the inherent uncertainty involved in the task make motion
prediction of surrounding vehicles a challenging problem. In this paper, we
present an LSTM model for interaction aware motion prediction of surrounding
vehicles on freeways. Our model assigns confidence values to maneuvers being
performed by vehicles and outputs a multi-modal distribution over future motion
based on them. We compare our approach with the prior art for vehicle motion
prediction on the publicly available NGSIM US-101 and I-80 datasets. Our
results show an improvement in terms of RMS values of prediction error. We also
present an ablative analysis of the components of our proposed model and
analyze the predictions made by the model in complex traffic scenarios.Comment: accepted for publication at IV 201
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles,
pedestrians and etc.) benefits the on-road motion planning for intelligent and
autonomous vehicles. Complex scenes always yield great challenges in modeling
the patterns of surrounding traffic. For example, one main challenge comes from
the intractable interaction effects in a complex traffic system. In this paper,
we propose a multi-layer architecture Interaction-aware Kalman Neural Networks
(IaKNN) which involves an interaction layer for resolving high-dimensional
traffic environmental observations as interaction-aware accelerations, a motion
layer for transforming the accelerations to interaction aware trajectories, and
a filter layer for estimating future trajectories with a Kalman filter network.
Attributed to the multiple traffic data sources, our end-to-end trainable
approach technically fuses dynamic and interaction-aware trajectories boosting
the prediction performance. Experiments on the NGSIM dataset demonstrate that
IaKNN outperforms the state-of-the-art methods in terms of effectiveness for
traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium
(IV) 202
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