LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
This work addresses autonomous driving by bridging low-level scene parsing and behavior-reflex approaches, though it appears incremental as it builds on existing sensor fusion and neural network methods.
The paper tackled the problem of generating driving paths by integrating LIDAR, GPS-IMU, and driving directions using a fully convolutional neural network, achieving a MaxF score of 88.13% in a 60x60 meter region and up to 92.60% in smaller regions.
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.