Open AccessPosted Content
Infrastructure Enabled Autonomy: A Distributed Intelligence Architecture for Autonomous Vehicles
TL;DR: In this article, a distributed intelligence architecture is proposed to partition the driving functions between the vehicle, edge computers on the road side, and specialized third-party computers that reside in the vehicle.
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Abstract: Multiple studies have illustrated the potential for dramatic societal, environmental and economic benefits from significant penetration of autonomous driving. However, all the current approaches to autonomous driving require the automotive manufacturers to shoulder the primary responsibility and liability associated with replacing human perception and decision making with automation, potentially slowing the penetration of autonomous vehicles, and consequently slowing the realization of the societal benefits of autonomous vehicles. We propose here a new approach to autonomous driving that will re-balance the responsibility and liabilities associated with autonomous driving between traditional automotive manufacturers, infrastructure players, and third-party players. Our proposed distributed intelligence architecture leverages the significant advancements in connectivity and edge computing in the recent decades to partition the driving functions between the vehicle, edge computers on the road side, and specialized third-party computers that reside in the vehicle. Infrastructure becomes a critical enabler for autonomy. With this Infrastructure Enabled Autonomy (IEA) concept, the traditional automotive manufacturers will only need to shoulder responsibility and liability comparable to what they already do today, and the infrastructure and third-party players will share the added responsibility and liabilities associated with autonomous functionalities. We propose a Bayesian Network Model based framework for assessing the risk benefits of such a distributed intelligence architecture. An additional benefit of the proposed architecture is that it enables "autonomy as a service" while still allowing for private ownership of automobiles.
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Citations
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Autonomous Driving without a Burden: View from Outside with Elevated LiDAR
TL;DR: In this article, the authors propose to use a coordinated set of LiDAR's outside at an elevation to provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency.
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Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments
TL;DR: The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning.
Patent
A system, a method for training a machine learning based processor circuitry suitable for characterizing an envi-ronment of a vehicle
Notz Dominik,Kuehbeck Thomas +1 more
- 10 Mar 2021
TL;DR: In this paper, a machine learning-based processor circuitry (114) was used for characterizing an environment of a vehicle (110) and a method (200) for training a machine-learning based processor circuitry(114) suitable for characterising an environment (110).
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Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles
TL;DR: Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls, and the modules of a general decision making framework and its variables are inferred.
References
Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations
TL;DR: In this article, the authors proposed a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy, which can be used to improve vehicle safety, congestion, and travel behavior.
3K
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On a Formal Model of Safe and Scalable Self-driving Cars
TL;DR: A white-box, interpretable, mathematical model for safety assurance, which the authors call-Sensitive Safety (RSS), and a design of a system that adheres to the safety assurance requirements and is scalable to millions of cars.
711
Challenges in Autonomous Vehicle Testing and Validation
Philip Koopman,Michael Wagner +1 more
TL;DR: Five major challenge areas in testing according to the V model for autonomous vehicles are identified: driver out of the loop, complex requirements, non-deterministic algorithms, inductive learning algorithms, and failoperational systems.
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