Multi-Session Visual Roadway Mapping

Abstract

This paper proposes an algorithm for camera based roadway mapping in urban areas. With a convolutional neural network the roadway is detected in images taken by a camera mounted in the vehicle. The detected roadway masks from all images of one driving session are combined according to their corresponding GPS position to create a probabilistic grid map of the roadway. Finally, maps from several driving sessions are merged by a feature matching algorithm to compensate for errors in the roadway detection and localization inaccuracies. Hence, this approach utilizes solely low-cost sensors common in usual production vehicles and can generate highly detailed roadway maps from crowdsourced data.

Exploring Self-Attention for Visual Intersection Classification

“…In [20], a situation resulting from future lane changes was predicted. In [21], a probabilistic multisession framework was developed using high-precision road maps for urban areas using low-cost sensors. In [22], intersection detection was studied as a binary classification problem using LSTM based on a monocular sequence.…”

The PRORETA 4 City Assistant System

“…The project was scheduled from 2015 to 2018 and four research assistants from three different institutes of TU Darmstadt worked together on this interdisciplinary project. Within this frame, several articles comprising new algorithms for driver intention detection and online driver adaptation [5][6][7][8][9], visual localization and mapping [10][11][12][13] and driver gaze target estimation [14][15][16][17] have been published as well as articles on safety approval of machine learning algorithms in the automotive context [18]. Many of the core ideas can be retrieved in the exemplary prototypical assistance system that is presented in this work.…”

“…On the project's website, additional information about the project and the final presentation event can as well be found. The project's research results beyond the City Assistant System are published in [5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”