Vast toolchain boosts testing of cooperative autonomous driving systems
VAST: V2X/Dynamic Map-Aware Autonomous Driving Systems Validation Toolchain
Robotics
Summary
Testing self-driving cars that communicate with other vehicles and traffic systems is complicated because many different software parts and sensors have to work together. The authors made VAST, a toolchain that connects several simulators and autonomous-driving software, to check how these systems behave together in different scenarios. Their tool helps find tricky situations where accidents could happen faster and more often, and shows that having detailed dynamic maps can make driving safer. VAST also runs efficiently even with multiple simulated cars around, making it a useful platform for testing real-world cooperative driving technology.
What this means in practice
- •For autonomous vehicle developers: Test and validate connected vehicle systems with integrated simulators and real software to find safety-critical scenarios faster.
- •For smart city infrastructure teams: Evaluate and improve cooperative traffic management systems by simulating interactions between vehicles and roadside sensors using dynamic maps.
Authors
Shunsuke Ito, Takuya Azumi
Abstract
Cooperative autonomous driving in the IoT-to-Edge-to-Cloud continuum requires system-level validation across vehicles, infrastructure sensors, edge-side Dynamic Map services, and in-vehicle autonomous-driving stacks. This paper presents VAST, a V2X/Dynamic Map-aware validation toolchain that connects Scenic, Scenario Simulator v2, AWSIM, Autoware, and SIM-LDM. VAST does not introduce a new search algorithm; instead, it addresses interoperability challenges, including Lanelet2-to-Scenic mapping, ROS 2-based co-simulation through SS2, Dynamic Map object injection into Autoware, and collection of TTC, PET, collision, timeout, and performance measurements. In occluded-intersection scenarios, Lanelet2-compatible constrained sampling increases the edge-case discovery rate from 40.0% to 80.0% and reduces the average time per discovered edge case from 259.7 s to 110.4 s. Under the same generated scenario distribution, Dynamic Map availability reduces the collision rate from 78.0% to 40.0% and increases non-collision outcomes from 22.0% to 60.0%, with statistically significant TTC/PET shifts. A throughput study with 1-16 NPCs shows that sampling remains below 0.1 s, whereas AWSIM/Autoware execution and restart overhead dominate runtime. These results position VAST as a practical validation infrastructure for cooperative autonomous-driving CPSs.