Multi-Modal Speed Profile Generation for Autonomous Navigation
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Solution Overview
Problem
Existing systems face challenges in developing typical speed profiles for multi-modal road segments, such as those before intersections or splits, where average or median speed calculations fail to represent the varying speeds of different driving options, leading to suboptimal performance in autonomous vehicles and predictive cruise control systems.
Innovation Solution
A method and apparatus that process probe data to determine multi-modality in travel speed on specific links of a travel network, separating speed profiles for different options such as driving straight or turning, and generating a travel speed map that associates these profiles with the corresponding segments, allowing for more accurate speed information for autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If average or median speed calculation is used for multi-modal road segments, then the calculation process is simple, but the speed profile does not represent the actual varying speeds of different driving options
Solution Approach 1:
The patent segments the speed data into multiple distinct speed profiles (e.g., through-traffic profile, turning profile, exiting profile) based on the multi-modal characteristics of the road segment. Instead of using a single average or median value, the system identifies and separates different speed distributions corresponding to different driving behaviors, thereby accurately representing the varying speeds of different driving options while maintaining computational feasibility through automated clustering algorithms.
2Measurement precision
If multiple speed profiles are determined for different driving options, then the speed profile accuracy is improved, but the processing complexity increases
Solution Approach 1:
The system employs automated algorithms that self-identify multi-modal characteristics and automatically generate multiple speed profiles without requiring manual intervention. The processing apparatus autonomously detects speed distributions, determines modality, separates different driving options, and creates corresponding speed profiles, thereby reducing the perceived complexity for users while maintaining high accuracy in representing various driving behaviors.
3Measurement precision
If probe data is processed to determine multi-modality, then the speed distribution characteristics are accurately identified, but the data processing time increases
Solution Approach 1:
The system performs preliminary processing of probe data to identify multi-modal characteristics and pre-generate multiple speed profiles for different driving options before they are needed for navigation. By advance-segmenting the speed data and storing pre-computed speed profiles for various road segments, the system reduces real-time processing requirements while maintaining accurate identification of speed distribution characteristics, thereby balancing accuracy with efficiency.
Data Source
AI summary
An approach is provided for separating past speed data into speed profiles corresponding to multi-modal segments of a travel network. A navigation processing platform determines one or more links of at least one travel network that includes at least one split into two or more links. The navigation processing platform processes and/or facilitates a processing of probe data associated with the one or more links to determine that the one or more links exhibit a multi-modality with respect to travel speed. The navigation processing platform then determines a plurality of speed profiles for one or more segments of the one or more links based, at least in part, on the multi-modality. The navigation processing platform further causes, at least in part, a generation of at least one travel speed map that associates the plurality of speed profiles with the one or more segments of the one or more links.