ComOpT　is　an　open-source　research　tool　for　coverage-driven　testing　of　autonomous　driving　systems,　focusing　on　planning　and　control.　Starting　with　(i)　a　meta-model　characterizing　discrete　conditions　to　be　considered　and　(ii)　constraints　specifying　the　impossibility　of　certain　combinations,　ComOpT　first　generates　constraint-feasible　abstract　scenarios　while　maximally　increasing　the　coverage　of　k-way　combinatorial　testing.　Each　abstract　scenario　can　be　viewed　as　a　conceptual　equivalence　class,　which　is　then　instantiated　into　multiple　concrete　scenarios　by　(1)　randomly　picking　one　local　map　that　fulfills　the　specified　geographical　condition,　and　(2)　assigning　all　actors　accordingly　with　parameters　within　the　range.　Finally,　ComOpT　evaluates　each　concrete　scenario　against　a　set　of　KPIs　and　performs　local　scenario　variation　via　spawning　a　new　agent　that　might　lead　to　a　collision　at　designated　points.　We　use　ComOpT　to　test　the　Apollo　6　autonomous　driving　software　stack.　ComOpT　can　generate　highly　diversified　scenarios　with　limited　test　budgets　while　uncovering　problematic　situations　such　as　inabilities　to　make　simple　right　turns,　uncomfortable　accelerations,　and　dangerous　driving　patterns.　ComOpT　participated　in　the　2021　IEEE　AI　Autonomous　Vehicle　Testing　Challenge　and　won　first　place　among　more　than　110　contending　teams.　©　2022　IEEE.