Autonomous Vehicle Speed Profiles for Safety-Risk Road Segments
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating safety risk situations on road segments due to difficulties in providing accurate speed limit information and speed profiles, especially in conditions like hazards, heavy traffic, or adverse weather, which can lead to increased safety risks and reduced performance.
Innovation Solution
A method and apparatus that generate dynamic speed profiles for autonomous vehicles by identifying road segments with safety risk profiles based on data such as traffic incidents, hazard warnings, and weather conditions, using aggregated probe data from vehicles traveling along the road segment to determine optimal speeds and navigation routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic speed profiles are generated using aggregated probe data from multiple vehicles, then accuracy of speed limit information is improved, but computational resources and data processing requirements increase
Solution Approach 1:
The system divides the road network into multiple road segments and processes probe data segment by segment. Each road segment is analyzed independently to generate speed profiles, which reduces the computational burden compared to processing all data at once while maintaining accuracy through localized analysis.
Solution Approach 2:
The system pre-processes and aggregates probe data from multiple vehicles before generating speed profiles. By collecting and organizing data in advance, the system reduces real-time computational requirements when actual speed profiles need to be generated for navigation.
2Reliability
If real-time speed profiles are generated for safety risk situations, then autonomous driving safety is improved, but data processing time and computational load increase
Solution Approach 1:
The system generates speed profiles specifically for road segments identified as having safety risks, rather than processing all road segments uniformly. This localized approach focuses computational resources on critical areas where safety improvements are most needed, reducing overall processing time while maintaining high safety standards.
Solution Approach 2:
The system uses aggregated probe data from vehicles that have already traversed road segments to provide feedback for generating speed profiles. This feedback mechanism allows the system to learn from actual vehicle performance and adjust speed recommendations, improving safety while reducing the need for extensive real-time computation.
3Reliability
If speed profiles are generated based on multiple data sources including traffic incidents and weather conditions, then navigation reliability is improved, but system complexity increases
Solution Approach 1:
The system merges multiple data sources including probe data from vehicles, traffic incident data, hazard warning data, weather condition data, and HD map data into a unified speed profile generation process. By integrating these diverse data sources, the system achieves high navigation reliability while managing complexity through a consolidated processing framework.
Solution Approach 2:
The system creates a multi-functional apparatus that can process various types of data (probe data, traffic incidents, weather conditions, HD map data) through a single speed profile generation mechanism. This universal approach handles multiple data sources using the same core processing logic, reducing system complexity compared to having separate processing systems for each data type.
Data Source
AI summary
A method, apparatus and computer program product are provided for generating speed profiles for autonomous vehicles in safety risk situations for a road segment. In this regard, a road segment associated with a safety risk profile is identified based at least in part on road condition data related to the road segment. Furthermore, probe data is obtained from one or more probe apparatuses traveling along the road segment associated with the safety risk profile. Based at least in part on the probe data, a speed profile for modeling velocity of autonomous vehicles along the road segment is generated. Additionally, an indication of the speed profile is provided to one or more autonomous vehicles to facilitate navigation of the one or more autonomous vehicles along the road segment.