(T) Following are some easy to understand summaries of how Waymo and Telsa are approaching the design, architectures, and modeling of their self-driving cars. Note how much they differ. 

Waymo – ChauffeurNet:

• Product offerings: Waymo One (self driving ride car service), Waymo Via (self driving truck)
• Physical inputs: Lidar, cameras, radar, sensors data
• Building-blocks:
  • Data sources: Use large data set of human-labeled images generated both by Waymo and by external vendors
  • Perception: Find road paths, traffic lights, obstacles..Leveraged network architecture search (NAS) to find quickly best architectures for models
  • Behavior Prediction: Leverage Google maps; train agents through ChauffeurNet (recurrent neural networks and reinforcement learning) to estimate trajectories in a simulated environment
  • Planning: Generate trajectories through ChauffeurNet based on feasibility, staying on the road, and avoiding collisions
  • Controls optimizer: Throttle and steering
• Source: ChauffeurNet presentation at Google I/O 2019

Tesla – HydraNet:

• Product offerings: Auto-pilot (keep the car in the lane), Smart Summons (the car can come to the passenger through a mobile app), Self-Driving
• Physical inputs: 8 cameras and sensors data (no lidar and no high definition maps)
• Building-blocks:
  • Data sources: collect data from Tesla cars and label them
  • First round of processing – HydraNet:
    • Each camera has the same HydraNet architecture:
      • Shared backbone for shared tasks – a modified ResNet 50 architecture – images are 3, 960, 1280
      • Heads for specific tasks – FPN/DeepLab/UNet architectures
    • Independant images from the 8 cameras can be used for instance in space and time for the layout of the scene
    • Dependant images are used from those cameras at the same time for instance to estimate certain factors such as depth
  • Second round of processing: features from the 8 HydraNets can feed a second model – recurrent – for road layout predictions
  • Training and inference: implement multi-task distributed training on custom-hardware – GPU cluster for training – and proprietary hardware cluster (code-named Dojo) for inference. On-going improvements of features through their deployments to car telemetry
• Source: Andrej Karpathy’s presentation at PyTorch 2019

Note: The picture above is Tesla’s model architecture.

Copyright © 2005-2020 by Serge-Paul Carrasco. All rights reserved.
Contact Us: asvinsider at gmail dot com.