63,690 research outputs found
Safety of the Intended Functionality Concept Integration into a Validation Tool Suite
Nowadays, the increasing complexity of Advanced Driver Assistance Systems
(ADAS) and Automated Driving (AD) means that the industry must move towards a
scenario-based approach to validation rather than relying on established
technology-based methods. This new focus also requires the validation process
to take into account Safety of the Intended Functionality (SOTIF), as many
scenarios may trigger hazardous vehicle behaviour. Thus, this work demonstrates
how the integration of the SOTIF process within an existing validation tool
suite can be achieved. The necessary adaptations are explained with
accompanying examples to aid comprehension of the approach
Grasping Causality for the Explanation of Criticality for Automated Driving
The verification and validation of automated driving systems at SAE levels 4
and 5 is a multi-faceted challenge for which classical statistical
considerations become infeasible. For this, contemporary approaches suggest a
decomposition into scenario classes combined with statistical analysis thereof
regarding the emergence of criticality. Unfortunately, these associational
approaches may yield spurious inferences, or worse, fail to recognize the
causalities leading to critical scenarios, which are, in turn, prerequisite for
the development and safeguarding of automated driving systems. As to
incorporate causal knowledge within these processes, this work introduces a
formalization of causal queries whose answers facilitate a causal understanding
of safety-relevant influencing factors for automated driving. This formalized
causal knowledge can be used to specify and implement abstract safety
principles that provably reduce the criticality associated with these
influencing factors. Based on Judea Pearl's causal theory, we define a causal
relation as a causal structure together with a context, both related to a
domain ontology, where the focus lies on modeling the effect of such
influencing factors on criticality as measured by a suitable metric. As to
assess modeling quality, we suggest various quantities and evaluate them on a
small example. As availability and quality of data are imperative for validly
estimating answers to the causal queries, we also discuss requirements on
real-world and synthetic data acquisition. We thereby contribute to
establishing causal considerations at the heart of the safety processes that
are urgently needed as to ensure the safe operation of automated driving
systems
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Scenario-based testing for the safety validation of highly automated vehicles
is a promising approach that is being examined in research and industry. This
approach heavily relies on data from real-world scenarios to derive the
necessary scenario information for testing. Measurement data should be
collected at a reasonable effort, contain naturalistic behavior of road users
and include all data relevant for a description of the identified scenarios in
sufficient quality. However, the current measurement methods fail to meet at
least one of the requirements. Thus, we propose a novel method to measure data
from an aerial perspective for scenario-based validation fulfilling the
mentioned requirements. Furthermore, we provide a large-scale naturalistic
vehicle trajectory dataset from German highways called highD. We evaluate the
data in terms of quantity, variety and contained scenarios. Our dataset
consists of 16.5 hours of measurements from six locations with 110 000
vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane
changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems
(ITSC) 201
Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification
Safety is an essential aspect in the facilitation of automated vehicle
deployment. Current testing practices are not enough, and going beyond them
leads to infeasible testing requirements, such as needing to drive billions of
kilometres on public roads. Automated vehicles are exposed to an indefinite
number of scenarios. Handling of the most challenging scenarios should be
tested, which leads to the question of how such corner cases can be determined.
We propose an approach to identify the performance boundary, where these corner
cases are located, using Gaussian Process Classification. We also demonstrate
the classification on an exemplary traffic jam approach scenario, showing that
it is feasible and would lead to more efficient testing practices.Comment: 6 pages, 5 figures, accepted at 2019 IEEE Intelligent Transportation
Systems Conference - ITSC 2019, Auckland, New Zealand, October 201
Ontology based Scene Creation for the Development of Automated Vehicles
The introduction of automated vehicles without permanent human supervision
demands a functional system description, including functional system boundaries
and a comprehensive safety analysis. These inputs to the technical development
can be identified and analyzed by a scenario-based approach. Furthermore, to
establish an economical test and release process, a large number of scenarios
must be identified to obtain meaningful test results. Experts are doing well to
identify scenarios that are difficult to handle or unlikely to happen. However,
experts are unlikely to identify all scenarios possible based on the knowledge
they have on hand. Expert knowledge modeled for computer aided processing may
help for the purpose of providing a wide range of scenarios. This contribution
reviews ontologies as knowledge-based systems in the field of automated
vehicles, and proposes a generation of traffic scenes in natural language as a
basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10
figure
Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges
The technology in the area of automated vehicles is gaining speed and
promises many advantages. However, with the recent introduction of
conditionally automated driving, we have also seen accidents. Test protocols
for both, conditionally automated (e.g., on highways) and automated vehicles do
not exist yet and leave researchers and practitioners with different
challenges. For instance, current test procedures do not suffice for fully
automated vehicles, which are supposed to be completely in charge for the
driving task and have no driver as a back up. This paper presents current
challenges of testing the functionality and safety of automated vehicles
derived from conducting focus groups and interviews with 26 participants from
five countries having a background related to testing automotive safety-related
topics.We provide an overview of the state-of-practice of testing active safety
features as well as challenges that needs to be addressed in the future to
ensure safety for automated vehicles. The major challenges identified through
the interviews and focus groups, enriched by literature on this topic are
related to 1) virtual testing and simulation, 2) safety, reliability, and
quality, 3) sensors and sensor models, 4) required scenario complexity and
amount of test cases, and 5) handover of responsibility between the driver and
the vehicle.Comment: 8 page
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