Following Distance Profiles for Crowdsourced Route Maps
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Solution Overview
Problem
Existing topological maps for autonomous driving lack the integration of following distances, which are crucial for enhancing driving comfort and safety by providing proactive distance maintenance from vehicles ahead.
Innovation Solution
A method to ascertain following distances as a function of position by receiving and processing measurement data from vehicles, distributing test points along routes, generating intersection lines, and allocating following distances to these points, enabling the enrichment of topological maps with empirical following distance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If topological maps are enriched with following distance information from crowdsourced trajectories, then driving comfort and safety are improved, but the complexity of data processing and map enrichment increases
Solution Approach 1:
The method segments the route into multiple sections based on test points distributed at predefined distances. Each section is independently analyzed for following distance characteristics, allowing complex route data to be processed in manageable segments rather than as a single large dataset
Solution Approach 2:
The system enables vehicles to automatically contribute their trajectory and distance data to the crowdsourcing pool without manual intervention. Each vehicle self-services by recording its own following distance measurements and position data, which are then automatically integrated into the topological map enrichment process
2Device complexity
If test points are distributed at constant distances along routes, then the structure of following distance profiles is simplified, but variable traffic situations cannot be captured at appropriate resolution
Solution Approach 1:
The system dynamically adjusts the distribution of test points along the route based on traffic situation variability. In areas with high variability or critical driving conditions, test points are distributed more densely, while in stable conditions, spacing is increased. This dynamic adaptation allows the profile structure to remain manageable while capturing essential traffic variations
Solution Approach 2:
The method changes the parameter of test point spacing from a fixed constant to a variable parameter that adapts to local traffic conditions. By modifying the distance between test points based on route characteristics and traffic variability, the system achieves both structural simplicity and measurement precision
Data Source
AI summary
A method for ascertaining following distances as a function of position by a control device. Measurement data of at least one trajectory along at least one route, having a multiplicity of measurement points including position data and distance data from vehicles that are ahead, are received, A group of test points along the route is distributed at pre-defined distances from one another. At each test point, an intersection line that is oriented transversely in relation to a course of the route is generated, Pairs of points corresponding to the intersection lines are ascertained from the multiplicity of measurement points. Based on the distance data, at least one following distance is allocated to the pairs of points corresponding to at least one intersection line. A control device, a computer program, and a machine-readable storage medium are also described.