1,451 research outputs found
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
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty
Progress towards advanced systems for assisted and autonomous driving is
leveraging recent advances in recognition and segmentation methods. Yet, we are
still facing challenges in bringing reliable driving to inner cities, as those
are composed of highly dynamic scenes observed from a moving platform at
considerable speeds. Anticipation becomes a key element in order to react
timely and prevent accidents. In this paper we argue that it is necessary to
predict at least 1 second and we thus propose a new model that jointly predicts
ego motion and people trajectories over such large time horizons. We pay
particular attention to modeling the uncertainty of our estimates arising from
the non-deterministic nature of natural traffic scenes. Our experimental
results show that it is indeed possible to predict people trajectories at the
desired time horizons and that our uncertainty estimates are informative of the
prediction error. We also show that both sequence modeling of trajectories as
well as our novel method of long term odometry prediction are essential for
best performance.Comment: CVPR 201
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