Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs

TL;DR

This paper introduces a deep learning approach combining CNNs and RNNs to improve dynamic object tracking and environment understanding in autonomous driving using fused laser data and occupancy grid maps.

Contribution

It presents a novel deep neural network architecture that integrates CNNs and RNNs for real-time object tracking and environment segmentation in 360-degree perception.

Findings

Achieved an AUC score of 0.946 for segmentation.

Improved detection of occluded objects.

Enhanced size estimation accuracy due to temporal data.

Abstract

The comprehensive representation and understanding of the driving environment is crucial to improve the safety and reliability of autonomous vehicles. In this paper, we present a new approach to establish an environment model containing a segmentation between static and dynamic background and parametric modeled objects with shape, position and orientation. Multiple laser scanners are fused into a dynamic occupancy grid map resulting in a 360{\deg} perception of the environment. A single-stage deep convolutional neural network is combined with a recurrent neural network, which takes a time series of the occupancy grid map as input and tracks cell states and its corresponding object hypotheses. The labels for training are created unsupervised with an automatic label generation algorithm. The proposed methods are evaluated in real-world experiments in complex inner city scenarios using the aforementioned 360{\deg} laser perception. The results show a better object detection accuracy in comparison with our old approach as well as an AUC score of 0.946 for the dynamic and static segmentation. Furthermore, we gain an improved detection for occluded objects and a more consistent size estimation due to the usage of time series as input and the memory about previous states introduced by the recurrent neural network.