Autonomous driving and modern driver assistance systems require highly accurate, up-to-date, and reliable environmental data. Vehicles must be able to reliably detect and spatially locate roadways, lanes, traffic signs, markings, intersections, curbs, and other objects in the traffic environment. JAWESO’s mobile mapping systems provide a precise mapping foundation for this purpose. The combination of high-resolution 360° image data, LiDAR point clouds, 3D data, and precise geodata creates detailed digital representations of roads, traffic areas, and urban infrastructure. This data supports the development, validation, and integration of technologies for autonomous vehicles, ADAS, HD mapping, and intelligent transportation systems.

One key application is the creation of HD maps and highly accurate digital road models. Simple maps are not sufficient for autonomous driving. Detailed information is required on lanes, road markings, traffic signs, traffic lights, curbs, intersections, junctions, bus stops, guardrails, traffic islands, and other objects in the road environment. Using mobile mapping, LiDAR data collection, and georeferenced image data, these elements can be precisely captured and made available for further processing. The resulting 3D point clouds provide measurable information on positions, distances, elevations, road alignments, and object geometries. This creates a robust foundation for high-definition maps, digital road networks, lane-level mapping, road asset mapping, and the development of automated driving functions.

Clear information about the road environment is crucial for autonomous systems. Of particular relevance are lane markings, traffic signs, traffic lights, stop lines, crosswalks, turning lanes, bike lanes, bus lanes, curbs, and structural boundaries. JAWESO systems enable detailed road environment mapping using image and point cloud data. This allows objects in the traffic environment to be digitally documented, classified, and utilized for further applications. This data is useful for object recognition, lane detection, traffic sign recognition, road inventory, training data, validation data, and the creation of data foundations for automated driving.

Autonomous driving functions require data that is both visually interpretable and geometrically precise. 360° panoramic images provide a realistic view of the road environment, while LiDAR data delivers dense point clouds for spatial analysis. Using GNSS, IMU, precise position data, and sensor fusion, the data is georeferenced and spatially localized with high accuracy. This results in high-quality geodata that can be reused in GIS systems, simulation environments, AI training processes, ADAS development, vehicle validation, and digital mapping processes. The combination of image data and 3D geometry supports the precise capture of road alignments, object positions, fields of view, and traffic infrastructure.

Before autonomous driving functions can be deployed in real-world traffic, they must be developed, tested, and validated. This requires realistic and accurate datasets. Mobile mapping data can help digitally map real-world road environments and make them usable for simulations, training data, or comparative analyses. The data collected with JAWESO supports applications such as ADAS validation, sensor validation, vehicle localization, environment mapping, object recognition, scenario analysis, test track documentation, and the development of AI models for mobility applications. The combination of visual image information, measurable 3D structures, and precise spatial mapping is particularly valuable in this context.

Autonomous and assisted vehicles must be able to navigate reliably within existing transportation networks. To do so, they require information about roadways, lane configurations, intersections, traffic rules, structural boundaries, and relevant objects in the surrounding environment. Precise road mapping, HD mapping, and georeferenced data enable traffic networks to be described in greater detail. This supports better vehicle localization, safer navigation, more accurate route planning, and the integration of automated systems into existing road infrastructure. An up-to-date and detailed data foundation is particularly important in complex urban areas with many road users, narrow streets, bike lanes, parking areas, and changing traffic patterns.

Digital data collection with JAWESO is ideal for companies, research institutions, automotive suppliers, technology providers, local governments, and infrastructure operators working on solutions for autonomous mobility and intelligent transportation systems. The data can be used for research projects, pilot routes, test beds for autonomous driving, smart mobility, connected mobility, C-ITS, traffic data analysis, digital infrastructure, and the further development of modern mobility concepts. This is also relevant for cities and operators: the better road spaces are digitally mapped, the easier it is to prepare automated mobility services, connected traffic systems, and new forms of traffic control.

JAWESO provides a reliable data foundation for autonomous driving, ADAS, HD maps, road space mapping, vehicle localization, and digital mobility applications. The combination of mobile mapping, LiDAR, 360° image data, 3D point clouds, geodata, and precise positioning enables detailed mapping of roads, lanes, and traffic infrastructure. This allows developers, research teams, technology providers, and infrastructure operators to better capture, analyze, and utilize real-world traffic networks for autonomous mobility.