department of electrical engineering, stanford university

Advanced Driver Assistant System Zihui Liu, Chen Zhu Department of Electrical Engineering, Stanford University Motivation Advanced driver assistant systems (ADAS) have been implemented in many vehicles to help increase both the safety of drivers and pedestrian. The related technology is also used to develop self- driving cars. Goal & Objective Traffic Sign Recognition Lane Deviation Detection Car make identification based on behind views of cars Car Make Identification Method 1: 2 Layer Neural Networks … … 0 1 2 softmax 1 = , 1ɸ = () FC – RELU 64 neurons FC 64 neurons Output label Input Input 64x64x3 3x3 Conv 64 Outputs 2x2 max pooling 64 outputs 3x3 Conv 128 Outputs 2x2 max pooling 128 outputs FC 1024 outputs Softmax 0 1 2 Output Labels 3 outputs 2x2 max pooling 512 outputs 2x2 Conv 512 outputs Method 2: Convolutional Neural Networks Dataset: 120 photos of each car make’s behind view Training Set: 100 photos of each car make randomly chosen from the dataset Testing Set : 20 photos of each car make randomly chosen from the dataset Acura ILX Honda Civic Hyundai Sonata Method Accuracy NN 75% CNN 78% Traffic Sign Recognition Method: Viola and Jones Detector Haar –like Feature extraction Cascade of Adaboost classifier Traffic Sign Detector Lane Detection & Stability Determination Edge Detection Hough Transform & Hough Peak Detection Lane Detection Future Work 1. More robust traffic sign recognition 2. Deeper CNN and larger car database Reference [1] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001, pp. I-511-I-518 vol.1.doi: 10.1109/CVPR.2001.990517 [2] Tensorflow Tutorial https://www.tensorflow.org/versions/r0.12/tutorials/index.html [3] LISA Traffic Sign Dataset http://cvrr.ucsd.edu/LISA/lisa-traffic-sign-dataset.html [4] Car make datasets are from Google image search

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Advanced Driver Assistant SystemZihui Liu, Chen Zhu

Department of Electrical Engineering, Stanford University

MotivationAdvanced driver assistant systems (ADAS) have been implemented

in many vehicles to help increase both the safety of drivers and

pedestrian. The related technology is also used to develop self-

driving cars.

Goal & Objective Traffic Sign Recognition

Lane Deviation Detection

Car make identification based on behind views of cars

Car Make Identification

Method 1: 2 Layer Neural Networks

… …

softm

𝒇1 = 𝒎𝒂𝒙 𝟎,𝑾1ɸ 𝒙𝒇𝟐 = 𝑾𝟐𝒇𝟏(𝒙)

FC – RELU

64 neurons

64 neurons Output label

Input Input

64x64x3

3x3 Conv

64 Outputs 2x2 max pooling

64 outputs

3x3 Conv

128 Outputs

2x2 max pooling

128 outputs

1024 outputs

Softmax 0

Output Labels

3 outputs

2x2 max pooling

512 outputs2x2 Conv

512 outputs

Method 2: Convolutional Neural Networks

Dataset: 120 photos of each car make’s

behind view

Training Set: 100 photos of each car make

randomly chosen from the dataset

Testing Set: 20 photos of each car make

randomly chosen from the datasetAcura ILX Honda Civic Hyundai Sonata

Method Accuracy

NN 75%

CNN 78%

Traffic Sign RecognitionMethod: Viola and Jones Detector

Haar –like Feature

extraction

Cascade of Adaboostclassifier

Traffic Sign Detector

Lane Detection & Stability Determination

Edge Detection

Hough Transform & Hough Peak Detection

Lane Detection

Future Work1. More robust traffic

sign recognition

2. Deeper CNN and

larger car database

Reference[1] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE Computer Society Conference on

Computer Vision and Pattern Recognition. CVPR 2001, 2001, pp. I-511-I-518 vol.1.doi: 10.1109/CVPR.2001.990517

[2] Tensorflow Tutorial https://www.tensorflow.org/versions/r0.12/tutorials/index.html

[3] LISA Traffic Sign Dataset http://cvrr.ucsd.edu/LISA/lisa-traffic-sign-dataset.html

[4] Car make datasets are from Google image search

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