1 research outputs found
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps (Masters Thesis)
One of the most important parts of environment perception is the detection of
obstacles in the surrounding of the vehicle. To achieve that, several sensors
like radars, LiDARs and cameras are installed in autonomous vehicles. The
produced sensor data is fused to a general representation of the surrounding.
In this thesis the dynamic occupancy grid map approach of Nuss et al. is used
while three goals are achieved. First, the approach of Nuss et al. to
distinguish between moving and non-moving obstacles is improved by using Fully
Convolutional Neural Networks to create a class prediction for each grid cell.
For this purpose, the network is initialized with public pre-trained network
models and the training is executed with a semi-automatic generated dataset.
The second goal is to provide orientation information for each detected moving
obstacle. This could improve tracking algorithms, which are based on the
dynamic occupancy grid map. The orientation extraction based on the
Convolutional Neural Network shows a better performance in comparison to an
orientation extraction directly over the velocity information of the dynamic
occupancy grid map. A general problem of developing machine learning approaches
like Neural Networks is the number of labeled data, which can always be
increased. For this reason, the last goal is to evaluate a semi-supervised
learning algorithm, to generate automatically more labeled data. The result of
this evaluation shows that the automated labeled data does not improve the
performance of the Convolutional Neural Network. All in all, the best results
are combined to compare the detection against the approach of Nuss et al. [36]
and a relative improvement of 34.8% is reached.Comment: This is the masters thesis of Florian Piewak. A shorter version of
this thesis was accepted at IV 201