Data Logic Structure and Key Technologies on Intelligent High-precision Map

doi:10.11947/j.JGGS.2020.0301

Abstract

Abstract:

Taking autonomous driving and driverless as the research object, we discuss and define intelligent high-precision map. Intelligent high-precision map is considered as a key link of future travel, a carrier of real-time perception of traffic resources in the entire space-time range, and the criterion for the operation and control of the whole process of the vehicle. As a new form of map, it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps. Thus, it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map. Accordingly, we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application. Next, we put forward the data logic structure of intelligent high-precision map, and analyze its application in autonomous driving. Then, we summarize the computing mode of “Crowdsourcing+Edge-Cloud Collaborative Computing”, and carry out key technical analysis on how to improve the quality of crowdsourced data. We also analyze the effective application scenarios of intelligent high-precision map in the future. Finally, we present some thoughts and suggestions for the future development of this field.

Key words: intelligent high-precision map; information transmission model; data logic structure; user model; computing mode; edge-cloud collaboration

Jingnan LIU, Jiao ZHAN, Chi GUO, Tingting LEI, Ying LI. Data Logic Structure and Key Technologies on Intelligent High-precision Map[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 1-17.

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Environ- mental monitoring	Level	Name	Definition	Scope	Data content	Map accuracy	Collection method	Map form	Map purpose
Human	L0	Non- automated	Completely human driving	No	Traditional map	10m	GPS trajectory+ IMU	Static map	Road navigation
	L1	Driver assistance	Single-functional assistance, such as ACC (Adaptive Cruise Control)	Limited	Traditional map	10m	GPS trajectory+ IMU			GPS trajectory+ IMU	Static map
	L2	Partial automation	Multi-functional assistance, such as LKA (Lane Keeping Assist)	Limited	Traditional map+ADAS data	1~5m				Active safety
	L3	Conditional automation	Automated driving under specific environments, human driver intervenes in an emergency	Limited	Static high-precision map	20~50cm	High-precision POS+image extraction			Static map+dynamic traffic information
	L4	High automation	Automated driving under specific environments, no human driver intervention	Limited	Dynamic high-precision map	5~20cm	High-precision POS+laser point cloud			Static map+dynamic traffic and event information and event information
L5	Full automation	Fully automated control of the vehicle	Any	Intelligent high-precision map		Multi-source fusion (professi-onal collection+crowdsourcing)	Static map+dynamic traffic and event information+analysis data

Data type	Content	Attributes	Geometric expression	Service function	Cooperation/ competition
Road network	Road topology, road geometry	Road direction, curvature, elevation, road type, number of lanes, ramp type, function level, etc.	Road reference line network (lines, points)	Global planning	Cooperation field
Lane network	Lane topology, lane geometry	Lane line, lane height, lane radius of curvature, lane width, lane direction, lane limit, etc.	Lane-level road network (lines, points)	Perception, positioning, local planning, vehicle control
Transportation facilities	Traffic signs, roadside facilities, fixed object	Type, height, width, color, shape, shape usage rules, shape classification, ID, etc.	Plane representation (points, lines, areas) Entity representation	Perception, positioning, local planning, vehicle control
Positioning layer	Multi-type positioning data (such as reflectance map)	Type, area, radius, color, reflectivity, feature height, etc.	Plane representation, entity representation	Positioning	Competitive field

Data type	Content	Attributes	Service function
Traffic restriction information	Road works, traffic control, traffic incidents, weather conditions, etc.	Road surface condition, visibility, limit start point, limit end point, limit length, influence range, lane ID, etc.
Traffic flow information	Real-time traffic con-gestion level, predict traffic congestion level, etc.	Passing time, congestion start point, congestion end point, congestion length, road travel time, congestion degree (color), lane ID, etc.	Dynamic path planning, vehicle control
Service area information	Parking space, load level of service area, etc.	Parking space width, parking space start point, parking space end point, parking space length, service area congestion degree (color), lane ID, etc.

Data type	Content	Attributes	Performance method (example)	Service function
Actively perceive dynamic information	Vehicle sensors actively sense nearby vehicles, pedestrians, traffic lights, etc.	Type, bearing, GNSS positioning data, distance, speed, course, etc.		Dynamic path planning, vehicle control
Passively perceive dynamic information	Nearby vehicles, pedestrians, traffic lights, etc. obtained from various sources other than vehicle sensors	Type, bearing, GNSS positioning data, distance, speed, course, etc.		Dynamic path planning, vehicle control

Data type	Content	Schematic diagram	Service function
Driving record data set	Vehicle configuration (sensor configuration, processing chip, communication equipment, vehicle performance, etc.) Scene information (natural environment, application, travel tasks, road conditions, etc.) Cognitive characteristics (personal age, cultural background, professional background, personalized needs, etc.) Driving behavior (horizontal and vertical control, following distance, etc.)		Personalized path planning
Driving experience data set	Hazardous area, speed configuration of characteristic road conditions, user needs, etc.		Personalized path planning