Autonomous Vehicle Caution Mode for Crowded Object Clusters
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating computationally-intensive and increased risk environments, such as crowded areas, where perceiving and tracking numerous objects requires significant computational resources and can pose safety risks.
Innovation Solution
The implementation of a caution mode system that determines whether to enter an operation mode with limited capabilities based on properties of object clusters, such as number, area, and proximity to the vehicle's planned route, allowing the vehicle to control its speed and restrict certain maneuvers to reduce computational load and enhance safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle perceives and tracks numerous objects in crowded environments, then the object detection capability is improved, but the computational resource consumption increases significantly
Solution Approach 1:
The patent segments the environment into clusters of objects based on spatial proximity and group characteristics. Instead of processing each object individually, the system groups multiple objects into clusters and processes them as unified entities, significantly reducing computational load while maintaining detection effectiveness in crowded areas
Solution Approach 2:
The system applies partial action by selectively processing only certain clusters based on their properties (size, density, proximity to vehicle). Not all clusters receive the same level of processing attention - only those that pose potential risks or require detailed analysis are processed in full detail, while others receive simplified handling
2Reliability
If the autonomous vehicle operates in caution mode with limited capabilities, then the safety is improved, but the operational efficiency decreases
Solution Approach 1:
The system dynamically adjusts operational capabilities based on real-time environmental assessment. The vehicle transitions between full operational mode and caution mode with limited capabilities depending on the presence and properties of object clusters. This dynamic adaptation allows the vehicle to maintain high efficiency in safe conditions while ensuring safety when risks are detected
Solution Approach 2:
The system changes operational parameters (speed limits, maneuver restrictions, detection thresholds) based on the assessed risk level. When operating in caution mode, parameters are adjusted to prioritize safety, but these changes are temporary and reversible when the environment becomes safer, thus balancing safety requirements with operational efficiency
3Measurement precision
If the autonomous vehicle clusters objects based on multiple properties, then the navigation accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct stages: initial object detection, spatial clustering based on proximity, property assessment of clusters, and risk evaluation. This segmentation breaks down the complex navigation decision-making into manageable steps, improving accuracy without overwhelming the processing system
Solution Approach 2:
The system applies different processing qualities to different clusters based on their local properties. High-priority clusters (those close to the vehicle, large in size, or dense) receive detailed analysis with multiple properties evaluated, while distant or sparse clusters receive simplified processing. This local quality approach optimizes the balance between navigation accuracy and processing complexity
Data Source
AI summary
Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.