Efficient graph-based V2V free space fusion

Abstract

A necessary prerequisite for future driver assistance systems as well as automated driving is a suitable and accurate representation of the environment around the vehicle with a sufficient range. To extend the range of the environment representation, sharing the environment detections of multiple vehicles via vehicle-to-vehicle (V2V) communication is a promising approach. In this paper, we present a method to fuse shared free space detections from multiple vehicles. The detections are represented as Parametric Free Space (PFS) maps, which are especially suitable for real-time radio V2Vtransmission due to their compactness. A graph-based algorithm to fuse PFS maps is proposed that solves possible contradictions between the maps and incorporates the maps' uncertainty attributes. By solely operating on the contour, the fusion can be carried out by a simple path search in a fusion graph that is constructed from the maps. This results in an efficient method that finds the fusion result within few iterations. To account for possible errors in the relative poses between the PFS maps, we furthermore present an adapted Iterative Closest Point (ICP) matching to align the maps before the fusion. Therein we employ a modified soft-assign scheme for robust outlier rejection, and incorporate the PFS maps' boundary orientation to improve the matching process. We show the capabilities of our method by presenting results on real test drive data.

An Overview of Attacks and Defences on Intelligent Connected Vehicles

“…Cooperative perception-based V2X provides an exciting opportunity for developing more reliable target recognition and tracking in the field of view (FOV) [27]. Furthermore, it can also provide an accurate perception of occluded objects [28], [29], [30] or objects outside the FOV by multimodal sensor data fusion [31], [32], [25].…”

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].…”