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Classification of Humans Driving BehaviorUsing Support Vector MachineGraduate School of Information ScienceEdahiro & Kato LaboratoryYuki Kitsukawayuki@ertl.jp1RWDA 2015: Project Work

Today, I want to talk about how the surrounding environment around the vehicle affects the drivers behavior.1

Background

UI

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BackgroundRealization of Human-Friendly Autonomous DrivingMachine Learning approach: Learn how human drives a car according to surrounding condition.

The objective of this project is to invest whether there is a relation between surrounding environment and drivers behavior especially I focused on the driver steps the brake pedal. 5

ObjectiveHypothesisIt is possible to judge how to control vehicles based on learning models.

Verification Method Support Vector MachineCreate learning model of surrounding environment and driving behaviorClassify Whether or not the driver steps the brake based on surrounding environmentIf there are pedestrian around the vehicle based on driving behavior

The objective of this project is to invest whether there is a relation between surrounding environment and drivers behavior especially I focused on the driver steps the brake pedal. 6

Dataset

Dataset

Grasshopper3 (Camera)

Velodyne HDL-64E (LIDAR)

Experimental vehicle

CardBUS (CAN)

Here, I will explain about the dataset. How I acquire the data. These are the sensors I used in this experiment. Grasshopper3 is the camera installed to capture the image of front of vehicle. This time the camera is used to detect pedestrians in front of the vehicle. Velodyne HDL-64E is the laser scanner installed on top of the vehicle to recognize the objects around the vehicle. This time velodyne is used to measure the distance from the vehicle to the pedestrian. CAN(Controller Area Network) signal is acquired through the CardBUS connected to the vehicle. From CAN signal, we can find the drivers behavior. For example, the velocity of the car, how the driver step the accel, brake, how degree the driver turn the steering and so on. In this project, I combined the data acquired through these sensors.8

The dataset

To acquire the data, I conducted field operation experiment in imitation city in Toyota. Combining the image and velodyne data, we can estimate the distance to the pedestrian.9

Dataset1-second intervals. 160 dataCAN signalcameravelodynevelocitysteeringanglegaspedalbrakepedal# ofpedestriandist. toPedestrianpedestrianbrake104.5034080001204.5313715129.65411304.53633200001404.51737590001504.502961000160.467.537177000072.0790309000083.27925704000194.069449340001104.179451075114.104711114.19301049133.49111124.06-43.504120001134.93-196.502520000145.28-321502690000155.12-433.5306350001

This is the dataset I acquired through experiment. Ispedestrian is the flag. If there are pedestrian captured by camera, it will be 1. Brakepress is the flag, if the driver steps the pedal, it will be 1.10

ANALYSis Method

Analysis MethodPattern 1surrounding environment driving behaviorInput: velocity, steering angle, # of pedestrian,distance to pedestrianOutput:0:not pedal brake, 1: pedal brake

Analysis MethodCAN signalcameravelodynevelocitysteeringanglegaspedalbrakepedal# ofpedestriandist. toPedestrianpedestrianbrake104.5034080001204.5313715129.65411304.53633200001404.51737590001504.502961000160.467.537177000072.0790309000083.27925704000194.069449340001104.179451075114.104711114.19301049133.49111124.06-43.504120001

Input

Output

This is the dataset I acquired through experiment. Ispedestrian is the flag. If there are pedestrian captured by camera, it will be 1. Brakepress is the flag, if the driver steps the pedal, it will be 1.13

Analysis MethodPattern 2 driving behavior surrounding environmentInput: velocity, steering angle, gas pedal,brake pedalOutput:0:no pedestrian, 1: pedestrian

Analysis MethodCAN signalcameravelodynevelocitysteeringanglegaspedalbrakepedal# ofpedestriandist. toPedestrianpedestrianbrake104.5034080001204.5313715129.65411304.53633200001404.51737590001504.502961000160.467.537177000072.0790309000083.27925704000194.069449340001104.179451075114.104711114.19301049133.49111124.06-43.504120001

InputOutput

This is the dataset I acquired through experiment. Ispedestrian is the flag. If there are pedestrian captured by camera, it will be 1. Brakepress is the flag, if the driver steps the pedal, it will be 1.15

analysis result

Pattern 1Positive: step brake pedal, Negative: not step brake pedal77.2%79.6%88.3%83.3%62.3%

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Pattern 2Positive: pedestrian, Negative: no pedestrians56.8%83.3%88.3%86.4%57.4%

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conclusion

Conclusion I researched the relationship between surrounding environment and driving behavior through classification using Support Vector MachineSurrounding Environment Driving BehaviorWhether to step break pedal: 88.3%Driving Behavior Surrounding EnvironmentWhether there is a pedestrian: 88.3%

Here, I want to conclude my project. I built the SVM classifier. The accuracy rate of pattern 1 is 77% and the pattern 2 is 56.7%.I can say that accuracy rate of pattern 1 is relatively high. In other words, It can be predictable whether the driver step the brake pedal according to the surrounding environment.However, it is difficult to estimate whether there is pedestrian from the CAN signal.To improve the accuracy rate, it is necessary to improve the detection of pedestrian. The pedestrian detection program used in this experiment often makes miss-detection. There is a room to improve the detection. The second is, here I took into consideration whether there is a pedestrian or not, so it is good way to think pedestruans direction, for example, the pedestrian is walk along the road or the pedestrians is about to cross the road. Third is capturing other vehicles round itself. The other is adding sensors, backward camera or laser scanner and so on. And considering other algorithm for machine learning.

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Future WorkFeature valueRelative Position of pedestrian, vehicleDriving area (traffic environment, city, rural area)Pedestrians directionTraffic LightVehicles destinationCollect more datasetParameter Tuning