HD map making companies
HD mapping is a niche space with many players. Companies like Here, TomTom, Civil Maps, Deepmap, Carmera, Nomoko, Dynamic Mapping Platform (DMP) are the mainstream companies. However, companies focusing on ADAS and AV technology are also building their own maps. These include Mobileye (Roadbook with Road Experience Management)4, Lyft, Toyota Research Institute Advanced Development’s Automated Mapping Platform.
HD map making space also has a consortium headed by HERE. This is known as the OneMap Alliance5 and like any other alliance, the aim is ensuring standardization of formats and cross platform usage.
AD/HAD without HD maps
A variant approach for autonomy is to avoid the usage of HD maps altogether, and build them up through immediate sensing. This approach can be highly scalable, as they can deployed anywhere without the tedious process of creation and maintenance of HD maps. First hurdle for this would be the technical challenges of this approach like complex intersections, unknown entities and edge cases etc. Secondly, several optimizations are possible with the availability of prior information, like improved vehicular management resulting in fuel efficiency with the knowledge of intersections, efficient navigation management of platoons etc. Thirdly, many ADAS functions such as Adaptive Headlights, Intelligent Speed adaptation depend on electronic horizon i.e. look-ahead information obtained from these maps. Lastly, live updates to these maps aid in real-time changes such as re-routing owing to accidents, construction zone blockages or other dynamic issues. An approach without HD maps would need to identify similar solutions to address these scenarios.
HD mapping for AVPGs
Autonomous Vehicle Proving Grounds (AVPG) are testing tracks for autonomous vehicles meant to verify their response to a multitude of scenarios in different driving conditions, and environments.6
The manmade structures in an AVPG include urban and rural roads, highway stretches, parking areas, cloverleaf or other complex intersections, urban canyons and notably a user-defined area. All but one of these structures are immovable and rigidly defined. The user-defined area is, as the name suggests, one that can be redefined to create certain infrastructure entities according to the envisaged scenario. Certain features7 or objects like traffic signals, Portable Variable Message Signs (PVMS), removable strips for lane markings/partitions, help create any environment in this user-defined area for the AV to be tested in. This poses an interesting problem for HD mapping companies - what is the strategy for a user-defined area? How different is the mapping problem from the introduction of PVMS in variable traffic conditions?
Well, when PVMS is introduced in variable traffic conditions or traffic cones are placed on a road to mark an accident zone, the connected vehicle technology helps. This information is updated on HD maps in the cloud and this update reaches all vehicles that follow the first vehicle. The case in AVPGs is very different, there is only one AV and it needs to be tested. For testing, the static elements of the environment need to reflect in the HD map it refers.
The proposed solution is a modular “plug and play” approach to the variants of these maps. Features whose positions are changing in the map are known in advance, as described above. Digital twins of these features or objects have to be created and stored in a library. This library has to be invoked at the time of updating the HD map before the AV test. The updates to this HD map can be stored as a pluggable layer, called the “Dynamic location layer”, which can be invoked by the end user as applicable. Depending on the complexity of the customization, the creation of the pluggable layer can be as simple as introducing a new entity into the HD map or a full-fledged data capture to create it.
Also, the Analytics layer, which provides cognitive capabilities in real world deployments, can be removed from HD maps to make the decision making more difficult in AVPG.
Can HD maps extend beyond autonomous vehicles?
One-day autonomous drones will use an advanced version of HD maps for delivery, especially when non line-of-sight drones get the necessary regulatory approvals. Locations will then not just be limited to two dimensions but will also include knowing the building floor for delivery by drones. Eventually the question that we are faced with is just how much mapping information is enough? Delivery by drones, in essence, will imply the usage of HD maps (as they stand today) combined with street views, in a quasi-three dimensional space.
Along similar lines, if delivery by autonomous terrain robots is to become a norm soon, then one of the concerns that need to be addressed is connectivity on pedestrian sidewalks. There is a high chance of pedestrian sidewalk not being continuous. In such cases, the city road infrastructure planners depend on a human being’s cognitive capabilities to figure out “how” to hop on from one segment of walkway to the next segment. Either, the plan has to be to code the same intelligence into all machines, or make pedestrian walkways better and then map well-connected paths for these autonomous machines.
The relevance of HD maps
Despite all the information that HD maps provide and numerous ways in which they aid decision making for AV, there is some dispute around whether or not the usage of HD maps is needed at all. Tesla for instance, has highlighted the issue of updates needed for HD maps, and claiming that they “can’t adapt”8, turn down the idea of HD maps. Whether Lidar is necessary to create HD maps is also under question with, for instance, Bosch creating Radar Road Signature together with TomTom9.
HD maps aid decision making by being additional source of information, which are handy in various edge cases (where instant sensing may not suffice). They also help AVs standardize learnings across various entities using the same map, thus driving uniformity in driving behaviors as well. HD maps are going to stay relevant.