Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct 30-31 Mario Gerla UCLA, Computer...
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![Page 1: Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct 30-31 Mario Gerla UCLA, Computer Science Dept.](https://reader035.fdocuments.in/reader035/viewer/2022062712/56649c7b5503460f9492f1aa/html5/thumbnails/1.jpg)
Driver Behavior Models
NSF DriveSense Workshop
Norfolk, VA Oct 30-31
Mario Gerla
UCLA, Computer Science Dept
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The Challenge
Safety• 33,963 deaths/year (2003)• 5,800,000 crashes/year • Leading cause of death for ages 4
to 34
Mobility• 4.2 billion hours of travel
delay• $78 billion cost of urban
congestion
Environment• 2.9 billion gallons of
wasted fuel• 22% CO2 from vehicles
![Page 3: Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct 30-31 Mario Gerla UCLA, Computer Science Dept.](https://reader035.fdocuments.in/reader035/viewer/2022062712/56649c7b5503460f9492f1aa/html5/thumbnails/3.jpg)
Will Driver behavior help?
• Can driver reaction models help reduce accidents?
• Can expected driver compliance help plan optimal routes, green waves and alternate transport modes?
• Can the knowledge of driver habits help plan pollution reduction strategies?
![Page 4: Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct 30-31 Mario Gerla UCLA, Computer Science Dept.](https://reader035.fdocuments.in/reader035/viewer/2022062712/56649c7b5503460f9492f1aa/html5/thumbnails/4.jpg)
Autonomous Vehicle Control
How much human control? Can drivers go to sleep?
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V2V for Platooning
Are drivers prepared to take over in case of attacks?
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V2V and cruise control to avoid Shockwave formations (INFOCOM 14)
VDR = Velocity Dependent Randomization: normal drive PVS = Partial Velocity Synchronization: advanced cruise control
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Intelligent navigation
• GPS Based Navigators• Dash Express (came to market in 2008):
• Synergy between Navigator Server and City Transport Authority
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NAVOPT – Navigator Assisted Route Optimization
• On Board Navigator– Interacts with the Server– Periodically transmits GPS and route– Receives route instructions
• Manhattan grid (10x10)– 5 routes (F1~ F5) from source to
destination– Link capacity: 14,925 [vehicles/h]
• But, will drivers comply?
S …
…
……
…
Shortest path
F1F3,4
F2
F3
F2,5
F5
F4
D
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Analytic Results
13500 13600 13700 13800 13900 14000 14100 14200 14300 14400 14500 14600 14700 148000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Total average delay (h/veh)
shortest path
flow deviation
Ave
rag
e d
elay
(h
ou
r)
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V2V for Safe navigation
• Forward Collision Warning, • Intersection Collision
Warning…….• Platooning (eg, trucks)• Advisories to other vehicles
about road perils– “Ice on bridge”, “Congestion ahead”,….
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V2V communications for Safe Driving
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 65 mphAcceleration: - 5m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc.
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.
Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.
Alert Status: None
Alert Status: Passing Vehicle on left
Alert Status: Inattentive Driver on Right
Alert Status: None
Alert Status: Slowing vehicle aheadAlert Status: Passing vehicle on left
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Existing sensors are about External Probing
• Radio Channels– DSRC– WiFI (V2V and V2I)– LTE; LTE Direct– White Spaces
But, radio channels can be attacked!
Autonomous vehicles currently use:• On Board Sensor Channels
– Laser, Lidar– Video Cameras– Optical sensors (reading encoded tail light signals)– GPS, accelerometer, acoustic, etc
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What about probing driver in the car?
• Driver Behavior important for efficient and safe navigation:
• A- Compliance models– Will driver comply with navigator instructions?– Will driver wait for Green Wave?– Will driver accept congestion fees?– Speed limits?`
• B- Reaction Time models– Can driver react fast enough to shockwave alerts?– Reaction to platoon accidents?
• C- Autonomous Car Driver models– Can the car estimate how long it will take to regain the attention of the
distracted driver?
• D. Physical Conditions Models– Detect sleepiness, predict medical situation etc
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How to build driver behavior model?
• Vehicle monitors the driver:– Collects from CAN bus relevant signals (brakes, accelerate, steer,
etc)– Body movements (video camera, kinect, etc)– Internal activities (music, phone calls, smoking, etc)
• Vehicle monitors other drivers and road traffic:– Correlation of driving behavior with external traffic
• Vehicle builds a model of the driver– Use machine learning techniques
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How is the driver model used?
• Autonomous vehicle uses the model to determine best action to avoid accidents:– Wake up driver or act directly on breaks?– Mimic driver behavior in autonomous driving
• Traffic authorities use aggregate models for planning– Aggregate model (for given age group, profession, place of residence, etc)
used to evaluate: • Congestion fee policies (for example)• Multimodal transport solutions• Road access control
– Privacy issue preserved by large number aggregation