TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios
Lan Feng,Quanyi Li,Zhenghao Peng,Shuhan Tan,Bolei Zhou,Lan Feng,Quanyi Li,Zhenghao Peng,Shuhan Tan,Bolei Zhou
Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called TrafficGen for traffic scenario generation. It learns from the fragmented human driving data collected in the real world and then generates realistic traffic scenarios. TrafficGen is an autoregressive neural generative model ...
