Autonomous Vehicle Command Controller with Probabilistic Action Selection
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Solution Overview
Problem
Conventional autonomous vehicles are sub-optimally designed, inefficient in resource utilization, 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 safety-critical functions.
Innovation Solution
A fleet of bidirectional autonomous vehicles with advanced sensors and communication systems, including redundant sensor fields and active lighting, that can self-drive, adapt to sensor failures, and invoke teleoperation for safety, optimizing navigation and inventory management through a centralized service platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous vehicles are designed with manual steering and driver seats, then they can accommodate licensed drivers and provide safety backup, but they are sub-optimally designed and require human intervention for safety-critical functions
Solution Approach 1:
The patent removes the steering wheel and driver seat from the vehicle interior, extracting the human driver component entirely. The vehicle is designed as a purpose-built autonomous transportation device without manual driving controls, eliminating the need for human intervention in safety-critical functions while maintaining reliability through advanced sensor systems and communication platforms
Solution Approach 2:
The autonomous vehicle performs all driving functions independently through automated navigation systems, sensor-based obstacle detection, and centralized fleet management. The vehicle serves itself by making autonomous decisions for steering, acceleration, braking, and route planning without requiring human drivers to perform safety-critical tasks
2Ease of operation
If conventional transportation services use privately-owned vehicles with human drivers, then passengers can access transportation services, but vehicles are under-utilized and inventory cannot be rebalanced to match demand
Solution Approach 1:
The autonomous vehicles serve multiple functions including passenger transportation, inventory redistribution, and demand-responsive routing. A single vehicle can service multiple passengers sequentially, redistribute itself to high-demand areas, and adapt its route dynamically based on real-time demand signals from the centralized platform, maximizing utilization efficiency
Solution Approach 2:
The vehicle routing and deployment is dynamically adjusted based on real-time demand patterns. The centralized service platform continuously monitors transportation requests and redistributes autonomous vehicles to locations where demand exceeds supply, enabling the fleet to adapt its inventory distribution dynamically rather than relying on static vehicle assignments
3Ease of manufacture
If conventional driverless vehicles are based on manually-driven automotive designs, then they can leverage existing automotive infrastructure, but they forego opportunities to simplify vehicle design and conserve resources
Solution Approach 1:
The vehicle interior is segmented into passenger seating areas without dedicated driver or passenger zones. The design divides the cabin into flexible seating configurations that can accommodate multiple passengers in various arrangements, eliminating the need for manual steering mechanisms and associated complexity while maintaining manufacturing feasibility through modular component assembly
Data Source
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 an autonomous vehicle fleet as a service. In particular, a method may include receiving, at an autonomous vehicle system, a command to control an ambient feature associated with the autonomous vehicle system. One or more courses of action may be determined based on the command. In addition, one or more probabilistic models associated with the one or more courses of action may also be determined. Based on the one or more probabilistic models, confidence levels may also be determined to form a subset of the one or more courses of action. A course of action from the subset of the one or more courses of action may then be executed at the autonomous vehicle system responsive to the command.