EP-4742191-A1 - ROAD TOPOLOGY DETECTION METHOD, AND RELATED APPARATUS

EP4742191A1EP 4742191 A1EP4742191 A1EP 4742191A1EP-4742191-A1

Abstract

A road topology detection method is provided and can be applied to the artificial intelligence field and the autonomous driving field. The method includes: obtaining target information, where the target information is data collected for a road condition of a target road; obtaining sequence information based on the target information by using a deep learning model, where the sequence information includes point information of feature points in a topology of the target road and edge information of a feature line in the topology of the target road, the point information includes physical positions and categories corresponding to the feature points, and the edge information includes a connection between the feature points and a connection direction; and determining the topology of the target road based on the sequence information. In this application, information about a node (the feature point) and an edge (the feature line) is directly predicted in one stage, thereby avoiding error accumulation of a whole road structure caused by errors in stages. In addition, data of the node and the edge is fused, to mutually reinforce each other in a network, thereby enhancing accuracy of a final result.

Inventors

CAI, Xinyue
CAI, Feipeng
ZHAO, Xinhai
XU, HANG
WEN, FENG

Assignees

Huawei Technologies Co., Ltd.

Dates

Publication Date: 20260513
Application Date: 20240717

Claims (20)

A road topology detection method, comprising: obtaining target information, wherein the target information is data collected for a road condition of a target road; obtaining sequence information based on the target information by using a deep learning model, wherein the sequence information comprises point information of feature points in a topology of the target road and edge information of a feature line in the topology of the target road, the point information comprises physical positions and categories corresponding to the feature points, and the edge information comprises a connection between the feature points and a connection direction; and determining the topology of the target road based on the sequence information.
The method according to claim 1, wherein the feature point is a road key point, a lane line key point, a lane centerline key point, or a road topology derivative point on the target road, and the feature line is a lane line segment, a lane centerline segment, a road segment, or a traffic entity.
The method according to claim 1 or 2, wherein the edge information further comprises a shape feature of the feature line.
The method according to any one of claims 1 to 3, wherein information about the topology further comprises an order of the feature points, and the order is consistent with an order obtained by performing a topological sorting method specified in a graph theory on the feature points.
The method according to claim 4, wherein the topological sorting method is depth-first traversal, breadth-first traversal, or a topological sorting method obtained through network learning.
The method according to any one of claims 1 to 5, wherein the category is one of the following: a start node, a relay node, a fork node, and a merge node.
The method according to any one of claims 1 to 6, wherein obtaining the sequence information based on the target information by using the deep learning model comprises: obtaining first sequence information and confidence of each basic unit in the first sequence information based on the target information and a first mask result by using the deep learning model, wherein the first mask result is an output mask result of an intermediate iteration process of the language model; masking a basic unit, in the first sequence information, whose confidence is less than a threshold, to obtain a second mask result; and obtaining second sequence information based on the target information and the second mask result by using the deep learning model, wherein the second sequence information is for obtaining the sequence information.
The method according to any one of claims 1 to 7, wherein the target information is image data or radar data, and pose information for collecting the image data or radar data.
A model training method, wherein the method comprises: obtaining sequence information, wherein the sequence information comprises point information of a first feature point and a second feature point in a topology of a target road and edge information of a first feature line and a second feature line in the topology of the target road, the point information comprises physical positions and categories corresponding to the first feature point and the second feature point, and the edge information comprises a connection between the first feature point and the second feature point and a connection direction; determining a prediction value of the point information of the second feature point and a prediction value of the edge information of the second feature line based on the point information of the first feature point and the edge information of the first feature line by using a deep learning model; and updating the language model based on the prediction values, the point information of the second feature point, and the edge information of the second feature line.
The method according to claim 9, wherein the sequence information is determined based on image data, radar data, or map data.
A data processing apparatus, wherein the apparatus comprises: an obtaining module, configured to obtain target information, wherein the target information is data collected for a road condition of a target road; and a processing module, configured to: obtain sequence information based on the target information by using a deep learning model, wherein the sequence information comprises point information of feature points in a topology of the target road and edge information of a feature line in the topology of the target road, the point information comprises physical positions and categories corresponding to the feature points, and the edge information comprises a connection between the feature points and a connection direction; and determine the topology of the target road based on the sequence information.
The apparatus according to claim 11, wherein the feature point is a road key point, a lane line key point, a lane centerline key point, or a road topology derivative point on the target road, and the feature line is a lane line segment, a lane centerline segment, a road segment, or a traffic entity.
The apparatus according to claim 11 or 12, wherein the edge information further comprises a shape feature of the feature line.
The apparatus according to any one of claims 11 to 13, wherein information about the topology further comprises an order of the feature points, and the order is consistent with an order obtained by performing a topological sorting method specified in a graph theory on the feature points.
The apparatus according to claim 14, wherein the topological sorting method is depth-first traversal, breadth-first traversal, or a topological sorting method obtained through network learning.
The apparatus according to any one of claims 11 to 15, wherein the category is one of the following: a start node, a relay node, a fork node, and a merge node.
The apparatus according to any one of claims 11 to 16, wherein the processing module is specifically configured to: obtain first sequence information and confidence of each basic unit in the first sequence information based on the target information and a first mask result by using the deep learning model, wherein the first mask result is an output mask result of an intermediate iteration process of the language model; mask a basic unit, in the first sequence information, whose confidence is less than a threshold, to obtain a second mask result; and obtain second sequence information based on the target information and the second mask result by using the deep learning model, wherein the second sequence information is for obtaining the sequence information.
The apparatus according to any one of claims 11 to 17, wherein the target information is image data or radar data, and pose information for collecting the image data or radar data.
A model training apparatus, wherein the apparatus comprises: an obtaining module, configured to obtain sequence information, wherein the sequence information comprises point information of a first feature point and a second feature point in a topology of a target road and edge information of a first feature line and a second feature line in the topology of the target road, the point information comprises physical positions and categories corresponding to the first feature point and the second feature point, and the edge information comprises a connection between the first feature point and the second feature point and a connection direction; a processing module, configured to determine a prediction value of the point information of the second feature point and a prediction value of the edge information of the second feature line based on the point information of the first feature point and the edge information of the first feature line by using a deep learning model; and an updating module, configured to update the language model based on the prediction values, the point information of the second feature point, and the edge information of the second feature line.
The apparatus according to claim 19, wherein the sequence information is determined based on image data, radar data, or map data.

Description

This application claims priority to Chinese Patent Application No. 202310911900.0, filed with the China National Intellectual Property Administration on July 21, 2023, and entitled "ROAD TOPOLOGY DETECTION METHOD AND RELATED APPARATUS", which is incorporated herein by reference in its entirety. TECHNICAL FIELD This application relates to the field of artificial intelligence, and in particular, to a road topology detection method and a related apparatus. BACKGROUND Artificial intelligence (artificial intelligence, AI) is a theory, a method, a technology, and an application system in which human intelligence is simulated, extended, and expanded via a digital computer or a machine controlled by a digital computer, to perceive an environment, obtain knowledge, and achieve an optimal result by using the knowledge. In other words, artificial intelligence is a branch of computer science, and seeks to learn essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence is to study design principles and implementation methods of various intelligent machines, so that the machines have perception, inference, and decision-making functions. The first step of autonomous driving is to collect and process environment information and in-vehicle information. Obtaining environment information around a vehicle body is the basis and prerequisite for autonomous traveling of an intelligent vehicle. This involves technologies such as road structure recognition, vehicle detection, and pedestrian detection. A road structure is one of the most important information of a road surface, and can effectively guide the intelligent vehicle to travel along a compliant route. A road network includes three parts: lane centerlines, road key points, and a centerline connection, to fully represent information of the road structure, and is an important form of representing the road structure. Real-time detection of the road network is an important link in an intelligent driver assistance system. This technology helps assist in functions such as route planning and lane departure warning, and may provide reference for accurate navigation. Road network prediction is a complex task that requires overall perception and inference of a scenario, and extraction of information of the three parts: the lane centerlines, road key points, and centerline connection. The information includes Euclidean information such as shapes and positions of the centerlines and key points, and non-Euclidean information such as a road topology structure. It is a great challenge to accurately predict the two types of information. In the conventional technology, the centerlines are first detected before the connection between the centerlines is predicted. The centerlines and the connection are separately predicted, and are not associated. The two-stage prediction causes information loss and error accumulation. For example, a deviation of the centerline detection and prediction causes inaccurate prediction of the connection, resulting in poor prediction accuracy of a road topology. SUMMARY This application provides a road topology detection method, to improve prediction accuracy of a road topology. According to a first aspect, this application provides a road topology detection method, including: obtaining data (namely, target information) collected for a road condition of a target road, and processing the target information by using a deep learning model, to obtain sequence information that can uniquely determine a topology of the target road, where the sequence information is represented as point information of feature points in the topology of the target road and edge information of a feature line in the topology of the target road, the point information includes physical positions and categories corresponding to the feature points, and the edge information includes a connection between the feature points and a connection direction; and determining the topology of the target road based on the sequence information. The deep learning model may be a language model or another deep learning model, or a model formed by combining these models in a serial, parallel, or cascade manner. In the conventional technology, centerlines are usually first detected before a connection between the centerlines is predicted or post-processed. The centerlines and the connection are separately predicted, and are not associated. The two-stage prediction causes information loss and error accumulation. For example, a deviation of the centerline detection and prediction causes less accurate prediction of the connection. In addition, two types of data cannot be fused for the two relatively separate stages of prediction. Fusion of the two types of data enables the model to better perceive a whole road network. Position information and shape information of the centerlines help predict the connection between the centerlines, and th