Mobile Robot Crosswalk Waiting Control for Traffic Light Occlusion
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Solution Overview
Problem
Autonomous robotic vehicles face challenges in navigating through dynamic environments, particularly when objects occlude their sensors' view of traffic lights, hindering their ability to determine whether it is safe to cross a crosswalk.
Innovation Solution
The robotic vehicle uses LIDAR and camera sensors to generate a bounding box or shell structure of objects, determining a visual occlusion zone and a reduced waiting area to position itself for unobstructed view of traffic lights, employing a processor to control its movement to maintain visibility of traffic indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the mobile robot uses camera sensors to monitor traffic lights, then it can detect traffic light states, but objects may occlude the field of view and prevent tracking
Solution Approach 1:
The system transitions from 2D image plane analysis to 3D spatial reasoning by projecting occlusion zones into the three-dimensional waiting area. The processor calculates visual occlusion zones based on object positions, bounding boxes, and camera parameters, then maps these zones into 3D space to determine safe waiting positions that maintain traffic light visibility.
Solution Approach 2:
The system performs preliminary calculation of visual occlusion zones and reduced waiting areas before the robot actually waits. By pre-computing which positions in the waiting area will maintain traffic light visibility and which will be occluded by objects, the robot can proactively select optimal positions rather than reactively adjusting after occlusion occurs.
2Reliability
If the mobile robot moves to maintain unobstructed view of traffic lights, then it can continue tracking, but it may delay crossing the crosswalk
Solution Approach 1:
The system calculates optimal waiting positions in advance, identifying areas where the traffic light remains visible without requiring continuous robot movement. By pre-determining these positions based on object locations and occlusion zones, the robot can wait efficiently without unnecessary repositioning delays.
Solution Approach 2:
The system uses its own camera parameters, field of view characteristics, and positioning data to autonomously calculate occlusion zones and determine optimal waiting positions. The robot serves itself by independently assessing which positions will maintain visibility without external assistance or complex coordination.
3Measurement precision
If the mobile robot generates a reduced waiting area by subtracting visual occlusion zones, then it can identify safe positions, but the available waiting area may be limited
Solution Approach 1:
The system applies different quality requirements to different spatial regions. Rather than requiring uniform visibility throughout the entire waiting area, the system identifies specific local zones (reduced waiting areas) where visibility is maintained. This allows the robot to utilize non-uniform spatial distribution of safe positions, maximizing the use of available space while maintaining tracking reliability.
Data Source
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AI summary
A mobile robot (306) and a method (700) for controlling a mobile robot are provided. The method includes: accessing a 3D map representation of an environment (200) including a respective indication of a crosswalk (302), a waiting area, and a visual traffic indicator; detecting an object located in the waiting area for generating a bounding box of the object; mapping a position of the bounding box to the 3D map representation; determining a visual occlusion zone of the object in the waiting area; determining a reduced waiting area as a difference between the waiting area and the visual occlusion area, and triggering control of the propulsion system for moving the mobile robot to the reduced waiting area.