Presentation on theme: "Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct 30-31 Mario Gerla UCLA, Computer Science Dept."— Presentation transcript:
Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct 30-31 Mario Gerla UCLA, Computer Science Dept
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% CO 2 from vehicles
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?
Autonomous Vehicle Control How much human control? Can drivers go to sleep?
V2V for Platooning Are drivers prepared to take over in case of attacks?
V2V and cruise control to avoid Shockwave formations (INFOCOM 14) VDR = Velocity Dependent Randomization: normal drive PVS = Partial Velocity Synchronization: advanced cruise control
Intelligent navigation GPS Based Navigators Dash Express (came to market in 2008): Synergy between Navigator Server and City Transport Authority
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 F1 F3,4 F2 F3 F2,5 F5 F4 D
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”,….
V2V communications for Safe Driving Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 65 mph Acceleration: - 5m/sec^2 Coefficient of friction:.65 Driver Attention: Yes Etc. Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 45 mph Acceleration: - 20m/sec^2 Coefficient of friction:.65 Driver Attention: No Etc. Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 75 mph Acceleration: + 20m/sec^2 Coefficient of friction:.65 Driver Attention: Yes Etc. Vehicle type: Cadillac XLR Curb weight: 3,547 lbs Speed: 75 mph Acceleration: + 10m/sec^2 Coefficient of friction:.65 Driver Attention: Yes Etc. Alert Status: None Alert Status: Passing Vehicle on left Alert Status: Inattentive Driver on Right Alert Status: None Alert Status: Slowing vehicle ahead Alert Status: Passing vehicle on left
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
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
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
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