Waymo's AI and Robotic Architecture: A Deep Dive with Novel Prediction Enhancements

摘要

Waymo, a leading autonomous vehicle company, has developed an advanced AI and robotic architecture to enable fully self-driving cars. This paper provides a deep dive into Waymo's system architecture, encompassing its sensor suite, high-definition mapping, perception, prediction, planning, and control modules. We review the deep learning models deployed for visual perception and behavior prediction, and discuss state-of-the-art approaches including long short-term memory (LSTM) networks and Transformers. A novel hybrid prediction model combining LSTM encoders with a Transformer-based interaction module is proposed to enhance trajectory forecasting accuracy for complex driving scenarios. We address practical challenges-such as handling rare edge cases, adverse weather, and real-time processing constraints-and examine ethical and policy considerations surrounding autonomous driving. The paper concludes with future outlooks for scaling Waymo's technology, integrating next-generation sensors and V2X connectivity, and the broader implications of safe, ubiquitous autonomous mobility. The insights and the proposed LSTM+Transformer enhancement aim to contribute to improved prediction performance and safety in Waymo's self-driving platform.