Adaptive 3D Mapping for Autonomous Vehicle Environment Changes
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Solution Overview
Problem
Conventional autonomous vehicles are sub-optimally designed, inefficient in resource usage, and poorly suited for managing inventory and rebalancing transportation services, with limitations in detecting and navigating social interactions, such as pedestrian and cyclist interactions, and require human intervention for complex scenarios.
Innovation Solution
A system and method for autonomous vehicles that include bidirectional designs with active lighting, advanced sensors, and a communication layer for real-time trajectory calculations and teleoperation, enabling efficient self-driving and fleet management, including redundant communication channels and a perception engine for object detection and path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional autonomous vehicles are designed to accommodate a licensed driver, then the vehicle can be controlled manually when needed, but the vehicle design becomes sub-optimal and resources are not conserved
Solution Approach 1:
The patent removes the driver's seat and manual control mechanisms from the vehicle, extracting only the essential autonomous driving functions. This eliminates the need to accommodate human drivers while maintaining full control capability through automated systems, thereby simplifying vehicle design without sacrificing control flexibility.
Solution Approach 2:
The vehicle is designed with universal control capabilities that can operate in both fully autonomous mode and manual control mode. The system integrates multiple functions into a unified platform that adapts to different operational requirements, allowing the same vehicle to serve diverse transportation needs without requiring separate designs for different control modes.
2Ease of operation
If conventional transportation services use privately-owned vehicles, then passengers can access transportation on-demand, but vehicle inventory management and rebalancing become inefficient
Solution Approach 1:
The patent implements a centralized fleet management system that continuously monitors vehicle locations, usage patterns, and demand signals. This feedback mechanism enables real-time tracking and analysis of vehicle inventory, allowing the system to optimize vehicle distribution and rebalancing based on actual operational data and predictive analytics.
Solution Approach 2:
The system performs preliminary routing and vehicle allocation based on predicted demand patterns before transportation requests are made. By pre-positioning vehicles in high-demand areas and planning optimal routes in advance, the system improves both transportation access and inventory management efficiency, reducing wait times and optimizing fleet utilization.
3Difficulty of detecting and measuring
If conventional approaches detect objects in the environment, then basic navigation is possible, but social interactions such as pedestrian and cyclist interactions are not sufficiently detected
Solution Approach 1:
The patent employs dynamic sensor arrays and processing systems that adapt to different environmental conditions and interaction scenarios. The detection system continuously adjusts its parameters based on the detected environment, enabling reliable identification of social interactions by modifying detection thresholds and algorithms in real-time based on contextual cues from pedestrians, cyclists, and other road users.
Data Source
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AI summary
Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide map data for autonomous vehicles. In particular, a method may include accessing subsets of multiple types of sensor data, aligning subsets of sensor data relative to a global coordinate system based on the multiple types of sensor data to form aligned sensor data, and generating datasets of three-dimensional map data. The method further includes detecting a change in data relative to at least two datasets of the three-dimensional map data and applying the change in data to form updated three-dimensional map data. The change in data may be representative of a state change of an environment at which the sensor data is sensed. The state change of the environment may be related to the presence or absences of an object located therein.