6 research outputs found
From Simulation Data to Test Cases for Fully Automated Driving and ADAS
Part 3: Practical Applications International audience Within this paper we present a new concept on deriving test cases from simulation data and outline challenging tasks when testing and validating fully automated driving functions and Advanced Driver Assistance Systems (ADAS). Open questions on topics like virtual simulation and identification of relevant situations for consistent testing of fully automated vehicles are given. Well known criticality metrics are assessed and discussed with regard to their potential to test fully automated vehicles and ADAS. Upon our knowledge most of them are not applicable to identify relevant traffic situations which are of importance for fully automated driving and ADAS. To overcome this limitation, we present a concept including filtering and rating of potentially relevant situations. Identified situations are described in a formal, abstract and human readable way. Finally, a situation catalogue is built up and linked to system requirements to derive test cases using a Domain Specific Language (DSL).
Document type: Part of book or chapter of boo
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
Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art
The large-scale deployment of automated vehicles on public roads has the
potential to vastly change the transportation modalities of today's society.
Although this pursuit has been initiated decades ago, there still exist open
challenges in reliably ensuring that such vehicles operate safely in open
contexts. While functional safety is a well-established concept, the question
of measuring the behavioral safety of a vehicle remains subject to research.
One way to both objectively and computationally analyze traffic conflicts is
the development and utilization of so-called criticality metrics. Contemporary
approaches have leveraged the potential of criticality metrics in various
applications related to automated driving, e.g. for computationally assessing
the dynamic risk or filtering large data sets to build scenario catalogs. As a
prerequisite to systematically choose adequate criticality metrics for such
applications, we extensively review the state of the art of criticality
metrics, their properties, and their applications in the context of automated
driving. Based on this review, we propose a suitability analysis as a
methodical tool to be used by practitioners. Both the proposed method and the
state of the art review can then be harnessed to select well-suited measurement
tools that cover an application's requirements, as demonstrated by an exemplary
execution of the analysis. Ultimately, efficient, valid, and reliable
measurements of an automated vehicle's safety performance are a key requirement
for demonstrating its trustworthiness