In this work, we propose VioHawk, a novel simulation-based fuzzer that hunts for scenarios that imply ADS traffic violations. Our key idea is that, traffic law regulations can be formally modeled as hazardous/non-hazardous driving areas on the map at each timestamp during ADS simulation testing (e.g., when the traffic light is red, the intersection is marked as hazardous areas). Following this idea, VioHawk works by inducing the autonomous vehicle to drive into the law-specified hazardous areas with deterministic mutation operations. We evaluated the effectiveness of VioHawk in testing industry-grade ADS (i.e., Apollo). We constructed a benchmark dataset that contains 42 ADS violation scenarios against real-world traffic laws. Compared to existing tools, VioHawk can reproduce 3.1X-13.3X more violations within the same time budget, and save 1.6X-8.9X the reproduction time for those identified violations. Finally, with the help of VioHawk, we identified 9+8 previously unknown violations of real-world traffic laws on Apollo 7.0/8.0. The paper titled "VioHawk: Detecting Traffic Violations of Autonomous Driving Systems through Criticality-guided Simulation Testing" has been accepted by ISSTA'24.