8/8/2019 Pomerleau Dean 1991 1

1/10

8/8/2019 Pomerleau Dean 1991 1

2/10

Figure 1: TheCMUNavlab Autonomous Navigation Testbed

2 Network Architecture

AtVINNs ament architecture consists of a single hidden layer back-propagationnetwork(setFigure2). The input layer ofthe networkconsistsofa30x32unit retinaonto which a video camera image isprojected Each ofthe 960units in the input retinais fully co~ec tcdto the hidden layer of5units.which is in turn fully co~ec tedtothe output layer. The output layer consistsof30 units and is a Linear representation of

the direction the vehicle should travel in order to keep the vehicle on the road. Thecentennostoutputunitreprtsentedthe travel 5Uaight ahead condition, while units tothe left and right ofcenterrepresented successively sharper left and right turns.

To drive the Navlab, a video image h r n the onboard camera is reduced to a low-resolution 30x32 pixel image and injected into the input layer After complemg aforwardpass through the networka Steeringcommand isread offthe output layer. Thesteering dircaiondictatedby the networkis taken to be the center ofmass ofthe Mofactivation surroundingthe output unitwith the highest activation level. Using thecenter ofmass ofaaivafioa instead ofthe most active output unit when determiningthe W o n to steerpermits finer steering comctions, and hence improves ALvNsdrivingacalmcy.

3 TrainingTo train ALM, the networkis presented with road images as input and the corre-spondingc o r n stetring direction as the desired output The weights in the networkare alteredusing the back-propagation algorithmso the networks output more closely

2

8/8/2019 Pomerleau Dean 1991 1

3/10

30x32 VInput Rc'Idco!t&n

Figure 2: ALVINNArchitecture

corresponds to the comct steering direction. The only modificationsto the standardback-propagation algorithm itselfused in this workare a weight change momentumfactor which is steadilyincreased during training, and a learning rate constant for eachweightwhich isscaledby the fan-inofthe unit towhich the weight projects. ALVIN"sability to learn quickly results from the output representation and the exemplar presen-tatioa scheme to bediscus& next.

Instead ofmining the network to activate only a single output unit. ALVI" ismined to produce a gausSian distribution ofactivation centend around the steering-on which will keep the vehicle centered on the road. As in the decode stage,thissteeringdinceionmay fallbetween the direcaons represented by twooutput units.The following approximation to a gaussian equation is used to precisely interpolate thec o r n output activation levels:

2xi= e 10-I

where x, represtnuthe deslred activationlevel foruniti andd , is the ith w t ' s distancefrom the c o m a stetring directionpoint along the outputveaor, which may fall betweentwo output units. The constant 10 in the above equation is an empirically-detexminedscale h a o r that detmmnes overhow many output units the gaussian shouldspread

3

8/8/2019 Pomerleau Dean 1991 1

4/10

8/8/2019 Pomerleau Dean 1991 1

5/10

8/8/2019 Pomerleau Dean 1991 1

6/10

8/8/2019 Pomerleau Dean 1991 1

7/10

8/8/2019 Pomerleau Dean 1991 1

8/10

Hidden Hidden Hidden Hidden Hidden

unit 1 unit2 Unit3 unit4 unit5

Epoch 0

Epoch 10

Epoch 20

Epoch 50

Figurt 5: The weights projecting fromthe input retina to the 5 hidden units in anALVINN network at four points duringGraining on a lined two-lanehighway. Blacksquares represent inhibitory weights and white square represent excitatoryweights.me diagonalblackandwhitebands on weights represent detectors for the yellow h edown the center and thewhite linedown the right edge ofthe road

8

8/8/2019 Pomerleau Dean 1991 1

9/10

8/8/2019 Pomerleau Dean 1991 1

10/10