Autonomous Vehicle Stop Planning Around Keep Clear Zones
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating through environments with designated 'keep clear' regions, such as railroad crossings, crosswalks, and intersections, where stopping is prohibited, and existing technologies struggle to prioritize these regions effectively to avoid accidents and ensure safe operation.
Innovation Solution
The implementation of a method and system that utilize pre-stored map information and real-time sensor data to generate a speed plan for autonomous vehicles, prioritizing 'keep clear' regions and adjusting the vehicle's trajectory to avoid stopping in these areas, while considering priority values and constraints to ensure safe and comfortable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle stops at all locations to ensure safety, then safety is improved, but the vehicle causes disruptions in keep clear regions such as railroad crossings and intersections
Solution Approach 1:
The system applies different stopping rules to different spatial locations by categorizing regions into keep clear regions (where stopping is prohibited) and non-keep clear regions (where stopping is permitted). The processors identify specific geographic areas as keep clear regions and enforce location-specific stopping constraints, allowing the vehicle to stop safely in appropriate areas while avoiding disruptions in prohibited areas.
Solution Approach 2:
The environment is segmented into distinct zones: keep clear regions where stopping is prohibited and non-keep clear regions where stopping is allowed. This segmentation allows the vehicle to apply different safety rules to different spatial segments, resolving the contradiction between universal safety and localized disruption avoidance.
2Object-affected harmful factors
If the vehicle avoids stopping in keep clear regions by using priority values, then disruptions are reduced, but the complexity of the control system increases
Solution Approach 1:
The system introduces a priority value parameter associated with each keep clear region to quantify the importance of avoiding stops in different areas. By assigning and comparing priority values, the processors can make automated decisions about where stopping is permissible, providing a systematic method to manage control complexity while effectively preventing disruptions in high-priority regions.
3Manufacturing precision
If the vehicle uses pre-stored map information to identify keep clear regions, then stopping accuracy is improved, but the requirement for detailed pre-stored map information increases
Solution Approach 1:
The system extracts only the essential information needed for safe operation—specifically, the geographic boundaries and priority values of keep clear regions—from the broader map data. By focusing on extracting and utilizing only this critical subset of map information, the system achieves high stopping accuracy without requiring or processing the entire comprehensive map dataset.
Data Source
AI summary
Aspects of the disclosure relate to generating a speed plan for an autonomous vehicle. As an example, the vehicle is maneuvered in an autonomous driving mode along a route using pre-stored map information. This information identifies a plurality of keep clear regions where the vehicle should not stop but can drive through in the autonomous driving mode. Each keep clear region of the plurality of keep clear regions is associated with a priority value. A subset of the plurality of keep clear regions is identified based on the route. A speed plan for stopping the vehicle is generated based on the priority values associated with the keep clear regions of the subset. The speed plan identifies a location for stopping the vehicle. The speed plan is used to stop the vehicle in the location.