The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short to mid-term trajectory prediction.
Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with.
Object detection algorithms for Lidar data have seen numerous publications in recent years, reporting good results on dataset benchmarks oriented towards automotive requirements.
Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products.
The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example.
With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware.
Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware.