Currently autonomous vehicles with some exceptions rely on on-board sensors for the detailed aspects of navigation. But many believe that high precision maps, which contain significantly more detailed information and true-ground-absolute accuracy than current road maps, will be essential for the safe operation of autonomous vehicles. These high-precision 3D maps will be specifically for the self-driving vehicle market and include detailed inventories of all stationary physical assets related to roadways such as road lanes, road edges, shoulders, dividers, traffic signals, signage, paint markings, poles, and all other critical data needed for the safe navigation of roadways and intersections by autonomous vehicles.  Referred to as high definition (HD) maps they will record the location of physical highway assets to centimeters.

These HD maps have two applications in the autonomous vehicle market.  They can be used by autonomous vehicles in actual driving to augment on-board sensors with near real-time and highly accurate data of the highway and surrounding location where the vehicle is driving.  Secondly because self-driving vehicles would need to travel billions of miles in the physical world to demonstrate a significant improvement on safety for human drivers, simulation has become an essential part of the development of autonomous vehicles.

 At the 2016 SPAR3D conference I had a chance to chat with Ron Singh, then Chief of Surveys at the Oregon Department of Transport about high definition (HD) maps of highway systems.  High definition means accurate to centimeters and Ron believed that HD highways maps would be required to reduce the risk and costs of highway construction.  But he also believed that autonomous vehicles would require this information.  Today autonomous vehicles rely completely on on-board sensors. but many believe that for safe operation they will require other information that can only come from external HD maps of highways and roads.

At the Year in Infrastructure 2017 (YII2017) conference in Singapore, Sharad Oberoi of Sanborn gave an insightful overview of the state of the art for automated feature extraction from combined aerial and mobile scans of highways and adjacent areas. Sanborn develops high precision maps for the autonomous vehicle market.  Sanborn has developed proprietary mapping technology that leverages aerial imagery, aerial lidar data, and mobile lidar data to create standardized, high-precision 3D base-maps focusing specifically on the self-driving vehicle market.

Recently in the UK Zenzic has commissioned the Ordnance Survey (OS) to explore the geospatial data requirements for HD maps when considering the lifecycle of testing and the data interoperability requirements to enable UK plc to provide an exemplar test facility ecosystem and set the foundations for operational deployments.  HD maps for autonomous vehicles are more complex than maps used for simple driver-based navigation systems. Mapping mustbe highly accurate (better than 5cmin resolution) and needs to contain a minimum set of road information, such as lane-level geometry and information relating to street furniture.This study has been conducted with the support of software simulation companies, the Met Office and the British Standards Institution(BSI), with the aim of understanding the data requirements, gaps and sources regarding geospatial data for self-driving vehicle testing.  A report Geodata report – analysis and recommendations for self-driving vehicle testing has been released.  It addresses why self-driving vehicles require real-time high precision maps. With high-definition maps which are updated in real-time, a self-driving vehicle is able to reference the position of other road users against what it already knows to be there. It also provides a back-up in situations where its sensors are unreliable such as poor weather conditions such as snow, heavy-rain or sun reflecting off a wet-road. 

Its key conclusions are that geospatial data is fundamental for autonomous vehicles. Maps can provide an important trusted baseline where the on-boardd sensor feed are unreliable.   To provide the framework required for the testing and driving of self-driving vehicles, current mapping specifications will need to be enhanced to include relevant street-level features, with better than 5cm resolution, more extensive information about street furniture, and interoperability with other sources of data.  An authenticated, authoritative single source of geospatial data accessible to autonomous vehicles is required. Standards relating to geospatial data in the context of autonomous vehicles are required. This is on the Open Geospatial Consortium radar. Real-time updates to mapping through sensor data feeds will also be essential.  Finally it makes some recommendations about the development of data formats (LAS 1.2 or LAZ, OBJ, OpenDrive, and ESRI shapefile), data hosting, governance, minimum safe requirements and standards.