Research presentation on Autonomous Driving. Direction perception approach. Research work by Princeton University group. Note: Link given in the presentation

1. Direct Perception in
Autonomous Driving

2. Introduction

3. Evolution in Car Technology

4. Evolution in Car Technology – 1st Gen
Manual Transmission
Combustion Engine
Basic Functions
Min Fuel Efficiency

5. Evolution in Car Technology – 2nd and Current Gen
Auto - Transmission
Hybrid Technology
Cruise Control
High Fuel Efficiency
Multimedia and GPS Nav

6. Evolution in Car Technology – Future Gen
Driver-less Intelligent Car
Autopilot Switching
Automatic Parking
Automatic Braking
Traffic Sign Recognition

7. 2 Paradigms for Autonomous Driving Today – Vision Based

8. Behavior Reflex
 Directly Map input image to steering angles using Neural Nets
 To learn the model, Human drives car on road and recorded images is training data
 Human decisions are unpredicted in similar situations makes difficult to learn
 Less accurate results

9. Mediated Perception
 Parse the entire scene to make driving decisions
 Recognize lanes, cars, traffic signs and lights, pedestrians etc.
 Combines results then send to AI based engine to make decision
 Current state of the art approach in Autonomous driving
 Used by Google car, Mobil eye etc.

10. Direct Perception
 Fall in b/w Mediated perception and Behavior reflex
 Based on deep ConvNet
 Estimate few key affordance indicators and compute driving commands based on them
 More accurate results and powerful autonomous experience

11. Implementation

12. TORCS – The Open Racing Car Simulator

14. Our Proposed System using CCN

15. Behavior Reflex

16. GIST Whole

17. Testing

18.  TORCS-based direct perception ConvNet on real driving videos taken by a
smartphone camera
 Trained and tested in two different domains
Testing on Real-World data - Smartphone Camera Video

19.  Lane perception module works perfectly
 Determine the correct lane configuration
 Car perception module is slightly noisier
 Computer graphics model of cars in TORCS look quite
different from the real ones
Results

20.  KITTI dataset contains over 40,000 stereo images
 For each image, we define a 2D coordinate system on the
zero height plane
 The “Y: axis is along the host car’s heading, while the “X”
axis is pointing to the right of the host car
 Estimate the coordinate (x, y) of the cars “ahead” of the
host car in this system
Car Distance Estimation on KITTI dataset

21.  Due to the low resolution of input images, cars far away can
hardly be discovered by the ConvNet
 Final distance estimation is a combination of the two
ConvNets Outputs
Results

22.  Compare the performance of our KITTI-based ConvNet with
the state-of-the-art DPM Car Detector
Comparison with DPM-based baseline

23.  On an image dataset of 21,100 samples
 4,096 neurons in the first fully connected layer
 Top 100 images from the dataset that activate the neuron
 What this neuron learned from training – Visual representation
Visualization

24.  We propose a autonomous driving paradigm based on
“Direct Perception”
 Experiments show that our approach can perform well in
both Virtual and Real environments.
Conclusions

25. By:
Chenyi Chen
Ari Seff
Alain Kornhauser
Jianxiong Xiao
Princeton University
Website: http://deepdriving.cs.princeton.edu
Original Research Work Titled:
Deep Driving: Learning Affordance in Autonomous Driving
Funded By:
Google
Intel
Nvidia Corp.

26. THANKS
Q&A