Autonomous Vehicle Navigation With Safe Stopping Distance Checks
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Solution Overview
Problem
Current autonomous vehicle navigation systems face challenges in ensuring safety and scalability, particularly in navigating proximity to pedestrian crosswalks, as they require standardized safety assurance and efficient processing of various environmental data sources while adhering to liability rules and constraints.
Innovation Solution
A navigation system that includes multiple image capture devices and processing units, capable of monocular and stereo image analysis, to detect pedestrians, traffic lights, and road markings, and determine navigational responses such as steering, braking, or acceleration, using machine learning and computer vision techniques to ensure safe vehicle operation near crosswalks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple image capture devices and processing units are used to accurately detect pedestrians and traffic lights near crosswalks, then safety and detection precision are improved, but device complexity and processing time increase
Solution Approach 1:
The navigation system divides the detection task into specialized modules: monocular image analysis module for general scene understanding, stereo image analysis module for depth perception, and specific detection modules for pedestrians, traffic lights, and road markings. This segmentation allows each module to focus on specific detection tasks, improving overall reliability while managing complexity through modular architecture
Solution Approach 2:
The patent introduces a crosswalk detection module as an intermediary that specifically identifies crosswalk regions and generates safety constraints. This intermediary module processes image data to detect crosswalk markings and generates navigational constraints that guide the vehicle's behavior in crosswalk proximity, improving safety without requiring complete reprocessing of all image data
2Measurement precision
If multiple image capture devices and processing units are used to accurately detect pedestrians and traffic lights near crosswalks, then detection precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary detection of crosswalk regions and generates safety constraints before detailed pedestrian and traffic light detection. The crosswalk detection module identifies crosswalk markings and establishes safety zones in advance, allowing subsequent detection algorithms to focus computational resources only within relevant regions, thereby improving detection precision while reducing overall processing time
Solution Approach 2:
The processing pipeline is segmented into parallel streams: monocular analysis for general scene understanding, stereo analysis for depth-critical objects, and specific detection streams for different object types. This segmentation allows simultaneous processing of multiple detection tasks, improving detection precision through specialized algorithms while managing processing time through parallel execution
3Reliability
If the system determines navigational responses such as steering, braking, or acceleration to ensure safe operation near crosswalks, then safety is improved, but the complexity of control operations increases
Solution Approach 1:
The navigation system applies different control strategies based on local conditions: when a pedestrian is detected within the safety constraint zone, the system applies specific braking or stopping actions; when no pedestrians are present, normal navigation proceeds. The crosswalk detection module generates location-specific safety constraints that guide appropriate control responses, improving safety through context-aware control while managing complexity through conditional logic
Data Source
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AI summary
Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processing device may be programmed to receive an image representative of an environment of the host vehicle, determine a planned navigational action for the host vehicle, analyze the image to identify a target vehicle with a direction of travel toward the host vehicle, and determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken. The at least one processing device may further determine a stopping distance for the host vehicle based on a braking rate, a maximum acceleration capability, and a current speed of the host vehicle, determine a stopping distance for the target vehicle based on a braking rate, a maximum acceleration capability, and a current speed of the target vehicle, and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.