Autonomous Vehicle Trajectory Assistance for Stuck Obstacle Cases
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently detecting and resolving stuck conditions due to obstacles, leading to resource wastage and poor user experience from false positives or prolonged waiting times.
Innovation Solution
The system includes a stuck condition detection mechanism in autonomous vehicles that sends assistance signals to an assistance center with low-level sensor data and high-level object representations, allowing the center to specify a new trajectory that avoids obstacles while adhering to navigational constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle sends assistance signals with detailed sensor information to the assistance center, then the accuracy of trajectory specification is improved, but the communication data volume and processing time are increased
Solution Approach 1:
The sensor information is segmented into two levels: low-level sensor strike input (raw data points) and high-level polygonal or polyhedral representations (processed object models). This segmentation allows the system to transmit only the essential processed information to the assistance center, rather than all raw sensor data, thereby reducing communication overhead and processing time while maintaining sufficient accuracy for trajectory specification.
Solution Approach 2:
The autonomous vehicle performs preliminary processing of sensor data by generating high-level polygonal or polyhedral representations of objects before transmission. This preliminary action filters and structures the data in advance, so that the assistance center receives pre-processed information ready for immediate trajectory calculation, reducing the overall processing time.
2Reliability
If the autonomous vehicle waits for expert intervention to resolve stuck conditions, then the safety is improved, but the user experience deteriorates due to prolonged waiting times
Solution Approach 1:
The system performs preliminary detection of stuck conditions by monitoring whether the vehicle can navigate according to the specified trajectory using sensor information. When a stuck condition is detected, the vehicle proactively sends an assistance signal with its current state and sensor data, enabling the assistance center to begin trajectory recalculation immediately rather than waiting for a timeout or manual trigger, thus reducing overall waiting time while maintaining safety.
Solution Approach 2:
The system implements a feedback loop where the autonomous vehicle continuously monitors its ability to follow the specified trajectory and automatically triggers assistance requests when obstacles prevent navigation. This feedback mechanism ensures that expert intervention is activated only when truly necessary, minimizing unnecessary delays while maintaining safety through continuous monitoring and automatic escalation.
3Productivity
If the system uses high-level polygonal or polyhedral representations of objects, then the computational efficiency is improved, but the detail and precision of obstacle detection are reduced
Solution Approach 1:
The system segments obstacle representation into two tiers: low-level sensor strike input that preserves all raw detection details for comprehensive obstacle characterization, and high-level polygonal or polyhedral representations that provide simplified geometric models for efficient computational processing. This dual-tier segmentation allows the system to maintain high detection precision through the low-level data while achieving computational efficiency through the high-level models during trajectory calculations.
Solution Approach 2:
The system transforms obstacle data from the detailed point-cloud dimension (sensor strikes) to the simplified geometric dimension (polygons/polyhedrons). This dimensional transformation enables efficient spatial reasoning and collision detection algorithms to operate on reduced-complexity representations while the original detailed data remains available for verification and edge-case analysis, balancing precision and efficiency.
Data Source
AI summary
An autonomous vehicle may include a stuck condition detection component and a communications component. The stuck-detection component may be configured to detect a condition in which the autonomous vehicle is impeded from navigating according to a first trajectory. The communications component may send an assistance signal to an assistance center and receive a response to the assistance signal. The assistance signal may include sensor information from the autonomous vehicle. The assistance center may include a communications component and a trajectory specification component. The communications component may receive the assistance signal and send a corresponding response. The trajectory specification component may specify a second trajectory for the autonomous vehicle and generate the corresponding response that includes a representation of the second trajectory. The second trajectory may be based on the first trajectory and may ignore an object that obstructs the first trajectory.