Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Abstract

Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

“…Recently, Zourlidou and Sester [21] conducted a systematic literature review on traffic-control detection from GPS tracks, highlighting (1) the importance of the topic itself, (2) the need for open data (GPS tracks and ground truth map) that researchers can use to test their methods and compare their results with others, (3) the low diversity of the predicted classes within each study and (4) the low percentage of studies that examine the cross-city applicability of their proposed methods (i.e., trained in city A and tested in city B). In this section, we briefly describe GPS-based methods for traffic regulator detection and identification.…”

“…Images or GPS tracks are commonly used for regulator detection and classification [21]. Traffic-sign detection is a popular topic within the computer-vision community, with some studies focusing on the traffic-sign classification such as [22] and others on related topics such as the prediction of traffic-signal phases [23].…”

Approaching Bike Hazards via Crowdsourcing of Volunteered Geographic Information

“…As the popular example from the OSM project shows, the approach of crowdsourcing georeferenced information can provide a broad data basis for decision making in many contexts. Nevertheless, concerning the benefits of crowdsourced data on questions of mobility, Zourlidou and Sester's recent literature review emphasizes the need for further research [5]. It can be an efficient method of data collection, especially for research work that involves the collection of data, perceptions, and evaluations of single individuals, i.e.…”

Recognition of Intersection Traffic Regulations From Crowdsourced Data

“…Related studies, mainly use either GPS tracks or images [15]. Traffic sign recognition from in-vehicle cameras is a popular topic in the computer vision community, providing accurate detection of traffic signs [16,17].…”

“…A systematic literature review of existing studies that use GPS traces for traffic regulation detection was conducted by Zourlidou and Sester [15]. Here we review only some distinct studies of the field.…”