The VAMLAV project is a Vinnova FFI-funded project with the following partners: AstaZero, Zenseact, RISE and AI Sweden. The project set out to create a dataset that includes the computer vision sensors that many Advanced Driver-Assistance System (ADAS) and Automated Driving Systems (ADS) vehicles use and complement them with a high-definition (HD)-map over a known geographic area. The VAMLAV dataset includes sensors such as camera, Light Detection and Ranging (LiDAR), Inertial Measurement Units (IMUs), and Global navigation satellite system (GNSS) sensors. This dataset, publicly available at AI Sweden, offers a corresponding HD-map in OpenDRIVE format covering the Rural Road at AstaZero. The dataset includes adverse weather, multiple maps and drives around the track with emulated traffic work scenarios that can occur. Beyond creating the dataset, the project aimed to validate HD-maps by comparing them to other measurement technologies. It delved deeper into localization for ADS vehicles by comparing various measurement campaigns and designing high-accuracy anchor points. This data was later used to validate and update the HD-map. By comparing different measurement systems and samples on the map, the project hopes to increase the trust in the HD-map over a longer time. This data also makes it possible to experiment more within the field of crowdsourced HD-maps from different systems while having an easier time measuring the accuracy of such maps. Another big part of the project was related to safety therefore some data was collected where the project emulates traffic work at AstaZero. This use case is otherwise difficult to test and evaluate due to the stochastic nature of traffic work in real life. Where the system detected the traffic work with the help of map and sensor data and then distributed the information to other cars in the area.