Steerable Sensor FOV Control for Occlusion-Aware Autonomous Driving
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Solution Overview
Problem
Autonomous vehicles face challenges in optimally positioning their steerable sensors to ensure effective navigation and obstacle avoidance, particularly when navigating through complex environments with varying terrain and occlusions, which can lead to suboptimal sensor field-of-view configurations.
Innovation Solution
The autonomy system employs a sensor field-of-view management system that selects a goal location based on various conditions, including proximity, occlusion, and route alignment, to dynamically adjust the sensor's field-of-view, ensuring optimal positioning and data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sensor field-of-view is fixed in a conventional position, then the device structure is simple, but the sensor cannot effectively detect critical areas in complex environments with occlusions and varying terrain
Solution Approach 1:
The patent implements a steerable sensor that can dynamically adjust its field-of-view position and orientation based on real-time environmental conditions. The sensor transitions from a fixed position to a dynamically adjustable position, allowing it to track obstacles, navigate around occlusions, and maintain optimal detection angles. This dynamic adjustment is controlled by an autonomy system that processes sensor data and generates steering commands.
Solution Approach 2:
The patent employs a feedback mechanism where the autonomy system continuously receives sensor data, evaluates the current field-of-view configuration against desired detection goals, and generates corrective steering commands. The system monitors obstacle positions, terrain variations, and occlusion levels, then adjusts the sensor orientation accordingly to maintain optimal detection coverage.
2Loss of information
If the sensor continuously adjusts its field-of-view to track all potential obstacles, then the obstacle detection coverage is maximized, but the processing complexity and computational load increase significantly
Solution Approach 1:
The patent applies local quality by directing the sensor's field-of-view to specific regions of interest rather than uniformly scanning the entire environment. The autonomy system identifies critical areas such as potential obstacle locations, navigation paths, and occlusion boundaries, then concentrates sensor resources on these specific zones. This selective monitoring reduces unnecessary data collection while maintaining comprehensive coverage of critical areas.
Solution Approach 2:
The system performs preliminary action by predicting potential obstacle locations and navigation challenges before they become critical issues. The autonomy system uses map data, vehicle trajectory information, and environmental models to anticipate where obstacles may appear or where occlusions may occur, then pre-positions the sensor field-of-view to maintain optimal detection angles in advance.
3Reliability
If the sensor field-of-view is adjusted dynamically based on complex environmental conditions, then the navigation reliability improves, but the response time and computational processing increase
Solution Approach 1:
The patent implements periodic action by updating the sensor field-of-view at optimized intervals rather than continuously. The autonomy system evaluates environmental changes and determines when repositioning is necessary based on detected motion, changing occlusion patterns, or approaching critical zones. This periodic adjustment reduces unnecessary processing while maintaining navigation reliability through timely updates.
Data Source
AI summary
Various examples are directed to systems and methods for directing a field-of-view of a first sensor positioned on an autonomous vehicle. In one example, at least one processor selects a goal location on at least one travel way in an environment of the autonomous vehicle. The selecting of the goal location is based at least in part on map data describing at least one travel way in an environment of the autonomous vehicle and pose data describing a position of the autonomous vehicle in the environment. The at least one processor determines a field-of-view position to direct the first sensor towards the goal location based at least in part on the sensor position data. The at least one processor sends a field-of-view command to the first sensor. The field-of-view command modifies the field-of-view of the first sensor based on the field-of-view position.