A Survey of Deep Learning Techniques for Autonomous Driving
Key Summary
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
Key Summary
- Data Sources - Cameras, radars, LiDARs, ultrasonic sensors, GPS units and/or inertial sensors
- 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
- 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
- Drivable area, pedestrians, traffic participants, buildings
- SegNet, AdapNet, Mask R-CNN
- 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)
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