Detecting traffic signs on images has many applications within the transportation area, e.g., speed limit detection, navigation, asset management, and autonomous vehicles. A significant challenge in detecting traffic signs is that a large set of labeled images are needed to train, test, and validate an object detector. In this paper, we demonstrate a webapp that enables traffic-sign detection of 169 common traffic signs and downloads the results. Users can contribute to improving object detection by (1) annotating user-uploaded images, (2) verifying traffic signs detected on existing imagery, or (3) donating new imagery by uploading images, e.g., taken with smartphones. The set of detected traffic signs is stepwise increased by correcting mistakes in object detection using the online verification part of the system. The user can immediately download all verified traffic-sign objects. Users can also download 43,995 traffic-sign objects from 378 different classes. This existing set of traffic-sign objects is updated nightly with objects two or more users have verified. The demonstration includes detecting traffic signs on imagery uploaded by the audience and downloading the traffic sign detected, e.g., to be used internally in an organization.

Images are an important data source for many applications such as autonomous driving and map annotations. However, it is expensive to collect large imagery sets and annotate objects such as traffic signs. In the paper, we present a web solution where a user efficiently can label traffic signs by verifying or correcting the labels provided by an object-detection model. All verified/corrected objects can immediately be downloaded by the user for usage in for example semi-supervised or supervised learning. The tool is available via the URL https://gomap.cs.aau.dk

Maintaining a database with the type, location, and direction of traffic signs is a labor-intensive part of asset management for many road authorities. Today there are high-quality cameras in cell-phones that can add location (EXIF) metadata to the images. This makes it efficient and cheap to collect large geo-located imagery datasets. Detecting traffic signs from imagery is also much simpler today due to the availability of several high-quality open-source object-detection solutions. In this paper, we use the detection of traffic signs to find both the location and the direction of physical traffic signs. Five approaches to cluster the detections are presented. An extensive experimental evaluation shows that it is important to consider both the location and the direction. The evaluation is done on a novel dataset with 21,565 images that is available free for download. This includes the ground-truth location of 277 traffic signs and all source code. The conclusion is that traffic signs are detected with an F1 score of 0.8889, a location accuracy of 5.097-meter (MAE), and a direction accuracy of ± 11.375°(MAE). Only data from two trips are needed to get these results.

Action cameras and smartphones have made it simple and cheap to collect large imagery datasets from the road network while driving. At the same time, several frameworks, e.g., Detectron2 and the TensorFlow Object Detection API, have made it fairly easy to build object-detection models for your imagery datasets. In this paper, we use the Detectron2 framework to detect 18 different common traffic signs from 351.469 images. The purpose is to automate the asset management of traffic signs in large road networks. A task that today often is done in a manual and labor-intensive manner. To improve the accuracy of determining the locations of traffic signs, we develop a new, general method that uses the size of the object detected (in pixels) and the camera's GPS position and heading. To further enhance the accuracy, multiple detections of the same physical traffic sign are clustered. The traffic-sign type and computed location are stored in a spatial data warehouse. The clustered locations are presented on a digital road network in a web app. This app allows visual inspection of the overall approach. We demonstrate that the accuracy of the computed locations is good, e.g., signs are placed on the correct side of the road or in/out of a roundabout.

Speed is a major killer in traffic. This paper presents the DriveLaB telematics platform that monitors drivers in real-time and provides feedback if they are speeding. The platform has been developed in collaboration with a major Danish insurance company and used in three larger field trials. The platform uses an Android/iOS smartphone app at the client side and does all data processing at the server-side, e.g., map-matching. The paper first provides an overview of the entire platform and then focuses on three major technical challenges: 1) A trip scoring algorithm that allows for comparison of scores across trip length. 2) A notification system that provides both positive and negative real-time feedback to the drivers, without leading the drivers' attention away from traffic. 3) Auto start of the cross-platform app to make it convenient for drivers to participate in the field trials. Three field trials, with 32 participants driving 57,933 km has been conducted. The results of these trials validates that trip scores can be compared across trip length. Further, we demonstrate that doing all data-processing on the server side is a viable approach also for real-time notifications and that drivers are notified with reasonable delays. Finally, we show that the client auto start is fast, robust, and convenient.

Speed is the major killer in traffic. The typical approach to enforce speed limits is by having the police monitor drivers and issue tickets when they are speeding. In this paper, we introduce a new platform where speeding is reduced by nudging. The three major approaches are to warn drivers if they are speeding, praise the drivers if they are driving within the speed limit, and grade each trip. The latter is used to rank drivers, e.g., drivers within a company are ranked according to their trip scores. We present the DriveLaB smartphone app that provides real-time feedback to the drivers. All computations are done at the server-side and we show how to compute real-time feedback and store trip data. In addition, we report on two field trials in the Copenhagen and Aalborg Areas where the platform is tested in collaboration with a major Danish insurance company.