Testing Autonomous Driving Systems -- What Really Matters and What Doesn't
Changwen Li, Joseph Sifakis, Rongjie Yan, Jian Zhang

TL;DR
This paper evaluates the effectiveness and validity of current testing methods for autonomous driving systems, highlighting their limitations and the influence of autopilot design on test outcomes, and advocates for more rational and deterministic autopilot development.
Contribution
It introduces a framework for comparing test methods, reveals their shortcomings, and emphasizes the importance of autopilot design principles for reliable testing.
Findings
Many test methods lack effectiveness and validity.
Most autopilots do not meet rationality and determinacy criteria.
Current testing cannot guarantee essential autopilot properties.
Abstract
Despite extensive research, the testing of autonomous driving systems (ADS) landscape remains fragmented, and there is currently no basis for an informed technical assessment of the importance and contribution of the current state of the art. This paper attempts to address this problem by exploring two complementary aspects. First, it proposes a framework for comparing existing test methods in terms of their intrinsic effectiveness and validity. It shows that many methods do not meet both of these requirements. Either because they are based on criteria that do not allow for rapid, inexpensive, and comprehensive detection of failures, or because the degree of validity of the properties tested cannot be accurately estimated. In particular, it is shown that most critical test methods do not take into account the nominal operational capabilities of autopilots and generate scenarios that…
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Taxonomy
TopicsVehicle Dynamics and Control Systems · Autonomous Vehicle Technology and Safety · Aerospace and Aviation Technology
