Vehicle Immobility Detection Using Situational Context and ODD Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in operating within their designed operational design domain (ODD) due to varying environmental conditions and sensor occlusions, which can lead to unsafe maneuvers and prolonged immobility without appropriate intervention.
Innovation Solution
An operational envelope detection (OED) framework that assesses the vehicle's trajectory and sensor capabilities, generating perception visibility models and occlusion maps to identify potential risks and initiate remedial actions, such as route adjustments or remote vehicle assistance, when the vehicle becomes immobile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle operates in varying environmental conditions without enhanced detection, then the system complexity remains lower, but the reliability of operation within ODD deteriorates
Solution Approach 1:
The system segments the detection process into multiple specialized modules: immobility detection module, situational context assessment module, and ODD evaluation module. Each module handles specific aspects of the detection task, improving reliability through dedicated functionality while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary assessment of situational context before making final immobility determinations. By pre-evaluating environmental factors, sensor data quality, and potential causes of immobility, the system builds a comprehensive understanding that enhances reliability of the final ODD operation assessment.
2Measurement precision
If the vehicle uses basic immobility detection without situational context, then the measurement process is simpler, but the measurement precision of immobility detection deteriorates
Solution Approach 1:
The system implements feedback loops where detection results are continuously refined based on situational context. The situational context assessment module provides feedback about environmental conditions and potential false causes of immobility, allowing the immobility detection module to adjust its measurements and improve precision.
Solution Approach 2:
The situational context assessment acts as an intermediary between raw sensor data and final immobility determination. This intermediary layer processes environmental information, sensor occlusion data, and trajectory information to filter out false positives and improve the precision of actual immobility detection.
3Loss of information
If the system implements comprehensive situational context assessment, then the loss of information about environmental factors is reduced, but the device complexity increases
Solution Approach 1:
The situational context assessment module serves multiple functions simultaneously: it evaluates environmental conditions, assesses sensor occlusion, analyzes trajectory data, and determines potential causes of immobility. This multi-functionality reduces information loss across multiple detection dimensions while managing complexity through a single integrated module.
4Productivity
If the vehicle waits longer to initiate remedial action, then the productivity impact is reduced, but the duration of immobility increases
Solution Approach 1:
The system performs preliminary evaluation of the immobility situation using situational context assessment before initiating remedial actions. By pre-evaluating whether the immobility is temporary or requires intervention, the system can make timely decisions that minimize both productivity impact and unnecessary delays, initiating actions only when truly needed.
Data Source
AI summary
Embodiments for operational envelope detection (OED) with situational assessment are disclosed. In some embodiments, a method comprises: determining at least one current or future constraint for a trajectory of a vehicle in an environment that is associated with the vehicle becoming immobile for an extended period of time; determining a stopping-reason for immobility of the vehicle based on determining the at least one current or future constraint for the trajectory of the vehicle in the environment; identifying a timeout threshold based on the stopping-reason, wherein the timeout threshold is an amount of time a planning system of the vehicle will wait before initiating at least one remedial action to address the immobility; identifying that the timeout threshold is satisfied; and initiating the at least one remedial action for the vehicle based on the identifying that the timeout threshold is satisfied.