  As autonomous driving systems (ADS) advance towards higher levels of
autonomy, orchestrating their safety verification becomes increasingly
intricate. This paper unveils ScenarioFuzz, a pioneering scenario-based fuzz
testing methodology. Designed like a choreographer who understands the past
performances, it uncovers vulnerabilities in ADS without the crutch of
predefined scenarios. Leveraging map road networks, such as OPENDRIVE, we
extract essential data to form a foundational scenario seed corpus. This
corpus, enriched with pertinent information, provides the necessary boundaries
for fuzz testing in the absence of starting scenarios. Our approach integrates
specialized mutators and mutation techniques, combined with a graph neural
network model, to predict and filter out high-risk scenario seeds, optimizing
the fuzzing process using historical test data. Compared to other methods, our
approach reduces the time cost by an average of 60.3%, while the number of
error scenarios discovered per unit of time increases by 103%. Furthermore, we
propose a self-supervised collision trajectory clustering method, which aids in
identifying and summarizing 54 high-risk scenario categories prone to inducing
ADS faults. Our experiments have successfully uncovered 58 bugs across six
tested systems, emphasizing the critical safety concerns of ADS.
