Vehicle Navigation With Pedestrian Path Prediction at Intersections
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Solution Overview
Problem
Current autonomous vehicle navigation systems face challenges in ensuring safety and scalability, particularly in processing and interpreting various data sources such as visual information, radar, lidar, and GPS data, while adhering to liability constraints.
Innovation Solution
The system employs cameras to provide autonomous vehicle navigation features by analyzing images to identify intersections, pedestrians, and determining navigational actions based on routing information, sensor data, and GPS data, while also predicting potential collisions and implementing collision mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle system processes and interprets multiple data sources (visual information, radar, lidar, GPS) to ensure safe navigation, then the safety and navigation accuracy are improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex navigation task into distinct functional modules: visual information processing, radar data processing, lidar data processing, GPS data processing, and integrated decision-making. Each sensor type and processing function is separated into independent modules that can be developed, tested, and maintained separately, reducing overall system complexity while maintaining comprehensive safety monitoring
Solution Approach 2:
The system employs a universal processing architecture that handles multiple data sources (visual, radar, lidar, GPS) through common processing pipelines and integration layers. This multi-functional framework allows the same computational infrastructure to process diverse sensor inputs and generate coordinated navigation decisions, reducing redundancy and simplifying system management
2Reliability
If the system adheres to liability rules and constraints in implementing navigational actions, then the legal compliance and safety assurance are improved, but the operational flexibility and response time may be reduced
Solution Approach 1:
The system pre-establishes liability rules, safety constraints, and ethical guidelines as part of its decision-making framework before actual navigation scenarios occur. These pre-programmed constraints are integrated into the navigation algorithm, allowing the vehicle to automatically evaluate actions against liability criteria in real-time without requiring manual intervention or complex legal analysis during critical moments
Solution Approach 2:
The system implements continuous feedback loops that monitor navigational actions against liability constraints and safety requirements. When potential violations are detected, the system provides feedback to adjust the navigation plan, ensuring compliance while maintaining operational flexibility through adaptive decision-making that learns from and responds to constraint boundaries
Data Source
AI summary
Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera, at least one captured image representative of features in an environment of the vehicle. The processor may identify an intersection and a pedestrian in a vicinity of the intersection represented in the image. The processor may determine a navigational action for the vehicle relative to the intersection based on routing information for the vehicle; and determine a predicted path for the vehicle relative to the intersection based on the determined navigational action and a predicted path for the pedestrian based on analysis of the image. The processor may further determine whether the vehicle is projected to collide with the pedestrian based on the projected paths; and, in response, cause a system associated with the vehicle to implement a collision mitigation action.