Waymo's Safety Methodologies and Safety Readiness Determinations
October 30, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nick Webb, Dan Smith, Christopher Ludwick, Trent Victor, Qi Hommes, Francesca Favaro, George Ivanov, Tom Daniel
arXiv ID
2011.00054
Category
cs.RO: Robotics
Citations
73
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Waymo's safety methodologies, which draw on well established engineering processes and address new safety challenges specific to Automated Vehicle technology, provide a firm foundation for safe deployment of Waymo's Level 4 ADS, which Waymo also refers to as the Waymo Driver. Waymo's determination of its readiness to deploy its AVs safely in different settings rests on that firm foundation and on a thorough analysis of risks specific to a particular Operational Design Domain. Waymo's process for making these readiness determinations entails an ordered examination of the relevant outputs from all of its safety methodologies combined with careful safety and engineering judgment focused on the specific facts relevant for a particular determination. Waymo will approve when it determines the ADS is ready for the new conditions without creating any unreasonable risks to safety. This paper explains Waymo's methodologies as applied to the three layers of its technology: hardware, ADS behavior, and operations, and also explains Waymo's safety governance. Waymo will continue to apply and adapt those methodologies, and to learn from the important contributions of others in the AV industry, as Waymo continues to build an ever safer and more able ADS.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Robotics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
π
π
The Cartographer
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
π
π
The Cartographer
Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
π
π
The Cartographer
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
R.I.P.
π»
Ghosted
Learning agile and dynamic motor skills for legged robots
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted