22,184 research outputs found
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing
There is an urgent societal need to assess whether
autonomous vehicles (AVs) are safe enough. From published
quantitative safety and reliability assessments of AVs, we know
that, given the goal of predicting very low rates of accidents,
road testing alone requires infeasible numbers of miles to
be driven. However, previous analyses do not consider any
knowledge prior to road testing – knowledge which could bring
substantial advantages if the AV design allows strong expectations
of safety before road testing. We present the advantages of a new
variant of Conservative Bayesian Inference (CBI), which uses
prior knowledge while avoiding optimistic biases. We then study
the trend of disengagements (take-overs by human drivers) by
applying Software Reliability Growth Models (SRGMs) to data
from Waymo’s public road testing over 51 months, in view of the
practice of software updates during this testing. Our approach is
to not trust any specific SRGM, but to assess forecast accuracy
and then improve forecasts. We show that, coupled with accuracy
assessment and recalibration techniques, SRGMs could be a
valuable test planning aid
Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles
Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular, results of operational testing with other evidence from design and verification. Growing use of machine learning in SCSs, by precluding most established methods for gaining assurance, makes evidence from operational testing even more important for supporting safety and reliability claims.
Objective: We revisit the problem of using operational testing to demonstrate high reliability. We use Autonomous Vehicles (AVs) as a current example. AVs are making their debut on public roads: methods for assessing whether an AV is safe enough are urgently needed. We demonstrate how to answer 5 questions that would arise in assessing an AV type, starting with those proposed by a highly-cited study.
Method: We apply new theorems extending our Conservative Bayesian Inference (CBI) approach, which exploit the rigour of Bayesian methods while reducing the risk of involuntary misuse associated (we argue) with now-common applications of Bayesian inference; we define additional conditions needed for applying these methods to AVs.
Results: Prior knowledge can bring substantial advantages if the AV design allows strong expectations of safety before road testing. We also show how naive attempts at conservative assessment may lead to over-optimism instead; why extrapolating the trend of disengagements (take-overs by human drivers) is not suitable for safety claims; use of knowledge that an AV has moved to a “less stressful” environment.
Conclusion: While some reliability targets will remain too high to be practically verifiable, our CBI approach removes a major source of doubt: it allows use of prior knowledge without inducing dangerously optimistic biases. For certain ranges of required reliability and prior beliefs, CBI thus supports feasible, sound arguments. Useful conservative claims can be derived from limited prior knowledge
Amused, accepted, and used? Attitudes and emotions towards automated vehicles, their relationships, and predictive value for usage intention
Automated vehicles (AVs) have left the laboratories and can be experienced in several projects, e.g. at the premises of a clinic in Germany. With this transition, research on AV attitudes no longer needs to rely on questionnaires with hypothetical scenarios and simulations. Previous research – limited by the unavailability of AVs – has provided ambivalent results regarding age and gender differences in attitudes towards AVs. We present research results about the role of age and gender in connection with attitudes such as acceptance, perceived safety, and trust, as well as intention to use. We additionally demonstrate relationships between those constructs and emotions such as amusement, fear, and surprise. Data were collected from participants (n = 125) after having experienced an AV ride with level 4 automation on two campuses of a clinic in Berlin, Germany. Results reveal strong correlations between all attitudes (0.55 ≤ r ≤ 0.71; p < 0.01) and show acceptance and perceived safety to be solid predictors of intention to use AVs. We also found age to be a significant predictor for usage intention even when other attitudes are considered (β = −0.22; p < 0.01). MANOVA results point to gender differences in all constructs, but with limited confidence (5.40 ≤ F ≤ 18.34; p ≤ 0.02). However, we reject our hypothesis that young men are highly accepting, trusting, and intending to use AVs compared to other combinations of age and gender. We recommend using a mix of attitude, emotion, and behavioural (intention) measures in future research on AVs together with more transparency regarding construct definitions and study materials
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Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 2
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure
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