879 research outputs found
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
In this work, a novel learning-based approach has been developed to generate
driving paths by integrating LIDAR point clouds, GPS-IMU information, and
Google driving directions. The system is based on a fully convolutional neural
network that jointly learns to carry out perception and path generation from
real-world driving sequences and that is trained using automatically generated
training examples. Several combinations of input data were tested in order to
assess the performance gain provided by specific information modalities. The
fully convolutional neural network trained using all the available sensors
together with driving directions achieved the best MaxF score of 88.13% when
considering a region of interest of 60x60 meters. By considering a smaller
region of interest, the agreement between predicted paths and ground-truth
increased to 92.60%. The positive results obtained in this work indicate that
the proposed system may help fill the gap between low-level scene parsing and
behavior-reflex approaches by generating outputs that are close to vehicle
control and at the same time human-interpretable.Comment: Changed title, formerly "Simultaneous Perception and Path Generation
Using Fully Convolutional Neural Networks
Multimodal 3D Semantic Segmentation
Understanding and interpreting a scene is a key task of environment perception for autonomous driving, which is why autonomous vehicles are equipped with a wide range of different sensors. Semantic Segmentation of sensor data provides valuable information for this task and is often seen as key enabler. In this report, we’re presenting a deep learning approach for 3D semantic segmentation of lidar point clouds. The proposed architecture uses the lidar’s native range view and additionally exploits camera features to increase accuracy and robustness. Lidar and camera feature maps of different scales are fused iteratively inside the network architecture. We evaluate our deep fusion approach on a large benchmark dataset and demonstrate its benefits compared to other state-of-the-art approaches, which rely only on lidar
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