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    [Deliverable 1: GPS App]

    [Deliverable 2: Image Feature Selection]

    [Deliverable 3: Accelerometer and Video Recording Apps]

    [Deliverable 4: Model Survey]

    [Deliverable 5: CS297 Report]

    [Menze Paper Review Slides]

    [Pomerleau Paper Review Slides]



CS297 Proposal

Crowd-sourcing Data for Autonomous Driving and Applying It

Yaoyan Xi (

Advisor: Dr. Chris Pollett


Autonomous driving is an artificial intelligence guided transportation mode that is capable sensing the environment and moving with little or no human input. It is a fledgling realm that will significantly transform the way people interact with the vehicles, vehicles interact with each other, and how the roads are built 'smarter'. It will enable more productivities from human drivers, decrease the traffic accidents, and potentially redistribute traffic volume to tackle the traffic congestion.

This project strives to build a mobile phone application that collects real-time road scenery and steering wheel movement data, both direction and steering wheel arc degrees, via sensors. It is a fact that every road has already been driven on by some one at some point and will likely be driven on by some one again in the future. It is also a fact that mobile phones are ubiquitous. So it seems promising to collect car driving data using a mobile application and then use that data to train autonomous vehicle AI systems. We will in this project develop applications to actually do this.

Our application will collect and integrate data such as lane, travel time/traffic volume, season/weather, speed limit, and GPS coordinates into the machine learning model to decide how to guide the steering wheel of autonomous vehicle.

We will use the Convolutional Neural Network (CNN) model and the training will be done through Python sklearn library.


Week 1: Jan. 27 - Feb. 2Kickoff meeting and proposal draft
Week 2: Feb. 3 - Feb. 9Proposal refinement and finalization
Week 3: Feb. 10 - Feb. 16Read Menze paper
Week 4: Feb. 17 - Feb. 23Start to build mobile application
Week 5: Feb. 24 - Mar. 2Continue to build mobile application
Week 6: Mar. 3 - Mar. 9Finish building App [Deliverable 1]
Week 7: Mar. 10 - Mar. 16Get the simulated dataset and preprocessing [Deliverable 2]
Week 8: Mar. 17 - Mar. 23Read Pomerleau paper
Week 9: Mar. 24 - Mar. 30Start to build CNN model
Week 10: Mar. 31 - Apr. 6Continue with CNN model [Deliverable 3]
Week 11: Apr. 7 - Apr. 13Start to validate CNN model built
Week 12: Apr. 14 - Apr. 20Finish validating CNN model built
Week 13: Apr. 21 - Apr. 27Start to integrate Google Map API and routines to control vehicles
Week 14: Apr. 28 - May 4Finish integrating Google Map API and routines to control vehicles [Deliverable 4]
Week 15: May 5 - May 11Start to write proposal report
Week 16: May 12 - May 18Finish writing proposal report [Deliverable 5]


The full project will be done when CS298 is completed. The following will be done by the end of CS297:

1. Understand state-of-the-art knowledge in autonomous/driverless vehicle and learn to build mobile application to sense vehicle's location

2. Secure the dataset suitable for this project from open sources and preprocess data via feature engineering

3. Build and train the CNN model

4. Validate the CNN model and integrate Google Map API and all routines for overall controls

5. CS297 proposal report writeup


[Long2007] A Review of Intelligent Systems Software for Autonomous Vehicles. Long,L., Hanford,S., Janrathitikarn, O., Sinsley, G., Miller, J. Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications. 2007.

[Menze2015] Object Scene Flow for Autonomous Vehicles. Menze,M.,Geiger,A. Computer Vision Foundation. 2015.

[Pomerleau1991] Efficient Training of Artificial Neural Networks for Autonomous. Navigation. Pomerleau, D.A. In Neural Computation 3:1 pp. 88-97. 1991.