4 research outputs found
DeepSignals: Predicting Intent of Drivers Through Visual Signals
Detecting the intention of drivers is an essential task in self-driving,
necessary to anticipate sudden events like lane changes and stops. Turn signals
and emergency flashers communicate such intentions, providing seconds of
potentially critical reaction time. In this paper, we propose to detect these
signals in video sequences by using a deep neural network that reasons about
both spatial and temporal information. Our experiments on more than a million
frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and
Automation (ICRA), 201
End-to-end Learning of Multi-sensor 3D Tracking by Detection
In this paper we propose a novel approach to tracking by detection that can
exploit both cameras as well as LIDAR data to produce very accurate 3D
trajectories. Towards this goal, we formulate the problem as a linear program
that can be solved exactly, and learn convolutional networks for detection as
well as matching in an end-to-end manner. We evaluate our model in the
challenging KITTI dataset and show very competitive results.Comment: Presented at IEEE International Conference on Robotics and Automation
(ICRA), 201