OSC2Runner is the first native orchestration framework mapping OpenSCENARIO v2.x DSL to CARLA via a multi-pass transpiler to dynamic behavior trees, claiming tick-by-tick determinism and exact trigger evaluation.
Language conditioned traffic generation
7 Pith papers cite this work. Polarity classification is still indexing.
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RTSGameBench is a new extensible benchmark for VLMs using diverse RTS matchups, diagnostic mini-games targeting individual competencies, and a self-evolving query-to-game generator, with results showing poor VLM performance on tight coordination and large-scale tasks.
ParaScale extracts a gauge-invariant Parallax Number from a reference video and re-realizes the same parallax against the target scene's depth map to achieve scale-calibrated camera motion transfer.
E² uses transport-regularized sparse control on learned reverse-time SDEs with topology-driven selection and Topological Anchoring to generate realistic adversarial scenarios, improving collision discovery by 9.01% on nuScenes and up to 21.43% on nuPlan while enabling closed-loop robustness gains.
AccidentSim creates videos of car collisions with physically accurate trajectories by simulating data from accident reports, fine-tuning an LM on those trajectories, and rendering with NeRF.
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
Framework uses LLMs for few-shot CARLA scenario code generation focused on collisions, followed by Cosmos-Transfer1 with ControlNet to produce realistic safety-critical driving videos.
citing papers explorer
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OSC2Runner: OpenSCENARIO 2.x Compliant High-Fidelity AV Simulation in CARLA
OSC2Runner is the first native orchestration framework mapping OpenSCENARIO v2.x DSL to CARLA via a multi-pass transpiler to dynamic behavior trees, claiming tick-by-tick determinism and exact trigger evaluation.
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RTSGameBench: An RTS Benchmark for Strategic Reasoning by Vision-Language Models
RTSGameBench is a new extensible benchmark for VLMs using diverse RTS matchups, diagnostic mini-games targeting individual competencies, and a self-evolving query-to-game generator, with results showing poor VLM performance on tight coordination and large-scale tasks.
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ParaScale: Scale-Calibrated Camera-Motion Transfer via a Gauge-Invariant Parallax Number
ParaScale extracts a gauge-invariant Parallax Number from a reference video and re-realizes the same parallax against the target scene's depth map to achieve scale-calibrated camera motion transfer.
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Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles
E² uses transport-regularized sparse control on learned reverse-time SDEs with topology-driven selection and Topological Anchoring to generate realistic adversarial scenarios, improving collision discovery by 9.01% on nuScenes and up to 21.43% on nuPlan while enabling closed-loop robustness gains.
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AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports
AccidentSim creates videos of car collisions with physically accurate trajectories by simulating data from accident reports, fine-tuning an LM on those trajectories, and rendering with NeRF.
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Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
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LLM-based Realistic Safety-Critical Driving Video Generation
Framework uses LLMs for few-shot CARLA scenario code generation focused on collisions, followed by Cosmos-Transfer1 with ControlNet to produce realistic safety-critical driving videos.