Auction-Based Cooperative Perception for AV Fleet Coordination
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Solution Overview
Problem
Autonomous and semi-autonomous driving systems face challenges in detecting objects due to occlusions and blind spots caused by environmental structures, leading to potential safety hazards, and existing centralized perception systems are prone to single-point failures and resource inefficiencies.
Innovation Solution
Implementing an auction-based cooperative perception system where connected vehicles distribute perception tasks and share data to enhance coverage and quality, reducing the risk of single-point failures and improving resource utilization through a decentralized yet balanced communication and computational approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized perception system is used, then coordination and data aggregation are simplified, but the system is prone to single-point failures and resource inefficiencies
Solution Approach 1:
The centralized perception system is segmented into distributed perception nodes across multiple vehicles. Each vehicle independently performs perception tasks using its own sensors and processing capabilities, eliminating the single-point failure risk of centralized systems while maintaining coordinated fleet-wide perception through selective data sharing.
Solution Approach 2:
An auction-based task allocation mechanism serves as an intermediary between perception tasks and vehicles. The auction system matches perception tasks with suitable vehicles based on their capabilities, current state, and resource availability, enabling efficient coordination without requiring a complex centralized architecture.
2Loss of information
If all vehicles continuously share perception data, then comprehensive coverage is achieved, but communication bandwidth and computational resources are wasted
Solution Approach 1:
Vehicles share perception data selectively based on local conditions and task requirements. Instead of uniform continuous sharing, the system determines which vehicles have relevant data for specific tasks and shares only that information, optimizing resource usage while maintaining comprehensive coverage when needed.
Solution Approach 2:
The system implements partial data sharing where only the necessary portion of perception data is transmitted. Vehicles perform perception tasks and share results with the auction system or requesting vehicles only when those results are relevant to current fleet needs, avoiding excessive communication and computation.
3Measurement precision
If perception tasks are assigned to the vehicle with best current capabilities, then task performance is optimized, but other vehicles remain underutilized
Solution Approach 1:
The auction-based task allocation system dynamically assigns perception tasks based on real-time vehicle capabilities, current state, and resource availability. Task assignment is not fixed but adapts to changing conditions, allowing different vehicles to take on tasks at different times based on their current suitability, thereby optimizing both task performance and overall fleet utilization.
Data Source
AI summary
This document describes techniques, apparatuses, and systems that can implement auction-based cooperative perception for autonomous and semi-autonomous driving systems. The described techniques, apparatuses, and systems cooperatively use perception systems of an autonomous or semi-autonomous vehicle (AV) in a fleet of connected AVs to provide perception data to the entire fleet. An AV of the fleet is selected to act as an auction system (e.g., an auctioneer) and sends an announcement offering perception tasks to the fleet for bidding. The AVs of the fleet determine whether they have the communication and computational capabilities to perform the tasks and, if so, submit bids to perform one or more tasks. The auctioneer awards the tasks, and the bid-winning AV(s) perform the tasks and update the fleet. In this way, the described techniques, apparatuses, and systems can provide perception services with increased coverage and quality, which can make autonomous and semi-autonomous driving systems safer.