A Survey of Deep Learning Techniques for Autonomous Driving

A Survey of Deep Learning Techniques for Autonomous Driving
Key Summary

• Data Sources - Cameras, radars, LiDARs, ultrasonic sensors, GPS units and/or inertial sensors

AI for different components

• Perception and Localization - Segmentation
• High-Level Path Planning  - Road Ahead / Turns 
• Behavior Arbitration, or low-level path planning - Steering
• Motion Controllers

Recurrent Neural Networks (RNN) are especially good in processing temporal sequence data, such as text, or video streams
Long Short-Term Memory (LSTM) [17] networks are non-linear function approximators for estimating temporal dependencies in sequence data
Reinforcement Learning using Partially Observable Markov Decision Process (POMDP)
formalism

• Perception and Localization - Segmentation
• Object detection and recognition, semantic segmentation
• Tesla® tries to leverage on its camera systems, whereas Waymo’s driving technology relies more on Lidar sensors4
• Waymo - 5 Radars, 8 Camera's
• Tesla - 8 Cameras, 12 ultrasonic sensors, one forward-facing radar

Driving Scene Understanding

• Single-stage detectors do not provide the same performances as double stage detectors but are significantly faster.
• The main disadvantage of using a LiDAR in the sensory suite of a self-driving car is primarily its cost

Semantic and Instance Segmentation

• Drivable area, pedestrians, traffic participants, buildings
• SegNet, AdapNet, Mask R-CNN

Localization

• Localization algorithms aim at calculating the pose (position and orientation) 
• The structure of the environment can be mapped incrementally with the computation of the camera pose - Simultaneous Localization and Mapping (SLAM)

Happy Learning!!!