Work on Local Dynamic Maps (LDM) implementation is still in its early stages, as the LDM standards only define how information shall be structured in databases, while the mechanism to fuse or link information across different layers is left undefined. A working LDM component, as a real-time database inside the vehicle, is an attractive solution to multi-ADAS systems, which may feed a real-time LDM database that serves as a central point of information inside the vehicle, exposing fused and structured information to other components (e.g., decision-making systems). In 5G-IANA we will be implementing a real-time LDM component, as a database reachable by vehicles through V2X communications and deployed in road-side units (RSU) or as on-board units (OBU), making use of the three pillars that guide a successful fusion strategy: utilisation of standards (with conversions between domains), middlewares to unify multiple ADAS sources, and linkage of data via semantic concepts.

Advanced Driver Assistance Functions (ADAS), such as Forward Collision Warning (FCW), Automated Braking (AB), Lane Departure Warning (LDW) or Blind Spot Detection (BSD) are already operational in our cars. Their utilisation in a vehicle has demonstrated exceptional performance to increase comfort and safety, with reductions of crashes between 27% to 50%. The research and innovation in ADAS and in Automated Driving (AD) functions is continuously growing and the market expects many new functions in the context of SAE-L3 vehicles. Particularly relevant will be the integration and coordinated co-existence of multiple functions, from multiple cars, potentially from different vendors, and with possibly overlapping capabilities, input needs and power consumption requirements.

The integration of an ecosystem of ADAS has become a major challenge, because their simple accumulation in a vehicle can produce undesired system conflicts and also decrease user acceptance because of the increased complexity of the vehicle. Car manufacturers are, therefore, keen to research on integrated solutions, which coordinate functions to operate harmonized, balance power and processing consumption, and overall improving driving safety.

One of the main challenges is the ability to fuse data from heterogeneous sources from diverse domains that have emerged and evolved independently from each other during the last decade: perception (e.g., sensing devices such as cameras or LIDARs with detection capabilities), communication (e.g., V2X systems, with standardized messages [1]), or digital maps (e.g., standard-definition or high-definition road topologies). The issue is that these domains have establish data formats, standards, and conventions for domain-specific use cases which suddenly clash into the common need to fuse multi-ADAS information real-time in a vehicle to provide the next step of autonomous driving.

Figure 1: iLDM implementation using Neo4j and RTMaps [3].

Provided each domain is governed by its own inertia, huge alignment efforts are required to interoperate perception, communication and digital map systems. Current approaches focus on creating inter-domain standards (e.g., ISO LDM, ASAM simulation branch), utilise multi-sensor application middlewares (e.g., RTMaps, ROS, ADTF) and apply semantic alignment of concepts [2].

One significant example which have attracted the attention of the automotive industry is the Local Dynamic Map (LDM) concept. Initially defined as an structure of road information categorized in layers (from static to dynamic information), methods to implement the structure itself have started to appear as a response to the publication of the LDM-related ETSI/ISO standards. In [3] an interoperable LDM (iLDM) implementation was presented, including a data model aligned with the recently published ASAM OpenLABEL standard [4], to leverage the interconnection of sensor data recording vehicle set-ups with ground truth generation.

Authors: Marcos Nieto, Mikel García, Itziar Urbieta (Vicomtech)


[1] ETSI EN 302 895 V1.1.1. Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Local Dynamic Map (LDM) Basic Set of Applications-ETSI EN 302 895. 2014. Available online: (accessed on 16 June 2021).

[2] Urbieta, I.; Nieto, M.; García, M.; Otaegui, O. Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO). Appl. Sci. 2021, 11, 7782.

[3] García, M.; Urbieta, I.;Nieto, M.; González de Mendibil, J.;Otaegui, O. iLDM: An Interoperable Graph-Based Local Dynamic Map.Vehicles 2022, 4, 42–59.

[4] ASAM. ASAM OpenLABEL V1.0.0. Available online: (accessed on 6 July 2021).