Autonomous Vehicle Obstacle Sharing to Reduce False Positives
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Solution Overview
Problem
Autonomous vehicle obstacle detection systems often produce false positives, leading to undesirable modifications in navigation paths or behaviors, which can be problematic especially when the detection is incorrect or unreal.
Innovation Solution
An autonomous vehicle system equipped with a sensor array and an artificial intelligence engine that generates a list of potential obstacles, allowing user intervention to verify true positives or false positives, and updates navigation paths or behaviors accordingly, with the ability to share obstacle information among vehicles for improved control and reduced sensor usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicle obstacle detection systems are used to detect obstacles in navigation paths, then obstacle avoidance capability is improved, but false positive detections occur leading to undesirable modifications in navigation paths or behaviors
Solution Approach 1:
The system implements feedback mechanisms where detection results are continuously evaluated and refined. The autonomous vehicle shares obstacle detection data with other vehicles and receives feedback about false positives, using this information to improve future detection accuracy and reduce unnecessary navigation modifications.
Solution Approach 2:
An intermediary verification system is introduced between the obstacle detection sensor and the navigation path modification system. This intermediary layer validates detected obstacles before triggering navigation changes, filtering out false positives and requiring higher confidence thresholds before modifying autonomous vehicle behavior.
2Measurement precision
If sensor arrays are used to detect obstacles in autonomous vehicles, then obstacle detection capability is improved, but system complexity and sensor usage increase
Solution Approach 1:
The patent combines data from multiple sensor types (lidar, radar, cameras) into a unified obstacle detection system. By merging sensor inputs and processing them through a common AI engine, the system achieves high detection precision while managing complexity through integrated processing rather than separate independent systems.
Solution Approach 2:
The sensor array is designed with multi-functionality, where the same sensors serve multiple purposes: obstacle detection, terrain mapping, and environmental sensing. This universal approach reduces overall system complexity by using a single sensor suite for multiple functions rather than dedicated sensors for each function.
3Extent of automation
If autonomous vehicles operate independently with individual obstacle detection systems, then vehicle autonomy is maintained, but obstacle detection accuracy decreases due to false positives
Solution Approach 1:
The system maintains vehicle autonomy while improving reliability through feedback loops where each vehicle's detection results contribute to a shared understanding of the environment. The autonomous vehicle receives feedback from other vehicles about obstacles and false positives, refining its own detection algorithms without human intervention.
Solution Approach 2:
A communication network acts as an intermediary between autonomous vehicles, enabling them to share obstacle detection data and verify findings collectively. This intermediary layer allows independent vehicles to cross-validate detections, reducing false positives while maintaining full autonomy without centralized control.
Data Source
AI summary
Embodiments of an autonomous vehicle are disclosed. In some embodiments, the autonomous vehicle includes a sensor array that can produce sensor data. The autonomous vehicle further includes a transceiver that can communicate with and receive data from at least a base station. Further, the autonomous vehicle includes a controller communicatively coupled with the sensor array and the transceiver. The controller includes code that receives a navigation plan data via the transceiver and navigates the autonomous vehicle along a navigation path specified in the navigation plan in such a manner as to avoid an interaction with one or more actual obstacles, in and around the navigation path, detected based at least in part on the sensor data.