12,384 research outputs found
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey
Scenario generation is one of the essential steps in scenario-based testing
and, therefore, a significant part of the verification and validation of driver
assistance functions and autonomous driving systems. However, the term scenario
generation is used for many different methods, e.g., extraction of scenarios
from naturalistic driving data or variation of scenario parameters. This survey
aims to give a systematic overview of different approaches, establish different
categories of scenario acquisition and generation, and show that each group of
methods has typical input and output types. It shows that although the term is
often used throughout literature, the evaluated methods use different inputs
and the resulting scenarios differ in abstraction level and from a systematical
point of view. Additionally, recent research and literature examples are given
to underline this categorization.Comment: accepted at IEEE IV 202
SOTIF-Compliant Scenario Generation Using Semi-Concrete Scenarios and Parameter Sampling
The SOTIF standard (ISO 21448) requires scenario-based testing to verify and
validate Advanced Driver Assistance Systems and Automated Driving Systems but
does not suggest any practical way to do so effectively and efficiently.
Existing scenario generation approaches either focus on exploring or exploiting
the scenario space. This generally leads to test suites that cover many known
cases but potentially miss edge cases or focused test suites that are effective
but also contain less diverse scenarios. To generate SOTIF-compliant test
suites that achieve higher coverage and find more faults, this paper proposes
semi-concrete scenarios and combines them with parameter sampling to adequately
balance scenario space exploration and exploitation. Semi-concrete scenarios
enable combinatorial scenario generation techniques that systematically explore
the scenario space, while parameter sampling allows for the exploitation of
continuous parameters. Our experimental results show that the proposed concept
can generate more effective test suites than state-of-the-art coverage-based
sampling. Moreover, our results show that including a feedback mechanism to
drive parameter sampling further increases test suites' effectiveness.Comment: accepted at IEEE ITSC 202
Misbehaviour Prediction for Autonomous Driving Systems
Deep Neural Networks (DNNs) are the core component of modern autonomous
driving systems. To date, it is still unrealistic that a DNN will generalize
correctly in all driving conditions. Current testing techniques consist of
offline solutions that identify adversarial or corner cases for improving the
training phase, and little has been done for enabling online healing of
DNN-based vehicles. In this paper, we address the problem of estimating the
confidence of DNNs in response to unexpected execution contexts with the
purpose of predicting potential safety-critical misbehaviours such as out of
bound episodes or collisions. Our approach SelfOracle is based on a novel
concept of self-assessment oracle, which monitors the DNN confidence at
runtime, to predict unsupported driving scenarios in advance. SelfOracle uses
autoencoder and time-series-based anomaly detection to reconstruct the driving
scenarios seen by the car, and determine the confidence boundary of
normal/unsupported conditions. In our empirical assessment, we evaluated the
effectiveness of different variants of SelfOracle at predicting injected
anomalous driving contexts, using DNN models and simulation environment from
Udacity. Results show that, overall, SelfOracle can predict 77% misbehaviours,
up to 6 seconds in advance, outperforming the online input validation approach
of DeepRoad by a factor almost equal to 3.Comment: 11 page
Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software
Simulation platforms facilitate the development of emerging Cyber-Physical
Systems (CPS) like self-driving cars (SDC) because they are more efficient and
less dangerous than field operational test cases. Despite this, thoroughly
testing SDCs in simulated environments remains challenging because SDCs must be
tested in a sheer amount of long-running test cases. Past results on software
testing optimization have shown that not all the test cases contribute equally
to establishing confidence in test subjects' quality and reliability, and the
execution of "safe and uninformative" test cases can be skipped to reduce
testing effort. However, this problem is only partially addressed in the
context of SDC simulation platforms. In this paper, we investigate test
selection strategies to increase the cost-effectiveness of simulation-based
testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC
coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies
to identify and skip test cases that are unlikely to detect faults in SDCs
before executing them.
Our evaluation shows that SDC-Scissor outperforms the baselines. With the
Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall
of 80% in selecting tests leading to a fault and improved testing
cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of
unnecessary tests as well as outperformed two baseline strategies.
Complementary to existing work, we also integrated SDC-Scissor into the context
of an industrial organization in the automotive domain to demonstrate how it
can be used in industrial settings.Comment: arXiv admin note: substantial text overlap with arXiv:2111.0466
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
SOTIF-Compliant Scenario Generation Using Semi-Concrete Scenarios and Parameter Sampling
Scenario-based testing is considered state-of-the-art to verify and validate
Advanced Driver Assistance Systems or Automated Driving Systems. Due to the
official launch of the SOTIF-standard (ISO 21448), scenario-based testing
becomes more and more relevant for releasing those Highly Automated Driving
Systems. However, an essential missing detail prevent the practical application
of the SOTIF-standard: How to practically generate scenarios for scenario-based
testing? In this paper, we perform a Systematic Literature Review to identify
techniques that generate scenarios complying with requirements of the
SOTIF-standard. We classify existing scenario generation techniques and
evaluate the characteristics of generated scenarios wrt. SOTIF requirements. We
investigate which details of the real-world are covered by generated scenarios,
whether scenarios are specific for a system under test or generic, and whether
scenarios are designed to minimize the set of unknown and hazardous scenarios.
We conclude that scenarios generated with existing techniques do not comply
with requirements implied by the SOTIF-standard; hence, we propose directions
for future research.Comment: accepted at IEEE ITSC 202
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