A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
Collision avoidance testing of the waymo automated driving system
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Racing-parameterized DRL policies for AV collision avoidance outperform an MPC-APF baseline in simulation across three scenarios, achieve zero-shot hardware transfer, and run at 31x fewer FLOPS with 64x lower latency.
citing papers explorer
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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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Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
Racing-parameterized DRL policies for AV collision avoidance outperform an MPC-APF baseline in simulation across three scenarios, achieve zero-shot hardware transfer, and run at 31x fewer FLOPS with 64x lower latency.