Autonomous Vehicles By: Rotha Aing

What makes a vehicle autonomous ? “Driverless” Different from remote controlled 3 D’s –Detection –Delivery –Data-Gathering

3D’s Detection – Reasoning –The surroundings and current conditions Data-gathering – Search –From the information search knowledgebase for purposed actions –What to do next? Delivery – Learning –View and record results of actions

Current Approaches Fully Autonomous –Taxi-like cars Autonomous in closed systems –Monorails Assistance System –Environment Sensing –Distance Sensors –ABS

Solution Template Sensors: Figure out obstacles around the vehicle Navigation: How to get to the target location from the present location Motion planning: Getting to the location, getting by any obstacles, following any rules Control: Getting the vehicle itself to move

Current Issues Technical –Sensors Understanding the environment –Navigation Know its current position and where it wants to go –Motion Planning Navigation through traffic –Actuation Operate the correct and needed features

Issues Social Issues –Trusting the car Getting on public roads Getting people to go in –Liability Issues –Lost Jobs

What’s been solved? Control Navigation Some issues of Sensory

Control Drive-By-Wire Sends messages to onboard computers Physical ties are unlinked In most current cars

Drive By Wire When sensor/trigger is pressed, it sends message to the car to perform the tasks

DBW in Autonomous Vehicles Replace the human driver Activate the sensors/triggers SciAutonics –Servomotors for each gear –Large servomotor with belt drive for steering

Navigation Already available Combination of: –GPS –Roadside database

Sensory Major issue: –Lack of computing power –“More processors” Half completed –RADAR –Laser Detection –Cameras

Sensory Information Issues Factors of weather –Dust, rain, fog Correctly Identifying an obstacle –Shadows vs. ditches –Shallow vs. deep Speed of the vehicle and the speed data can be correctly received

Motion Planning Most challenging Collision Detection Affected by: –Quality of Sensory information –Quality of Controls Need for algorithm that can determine movements quickly but also the correct ones

“Road Map” Decision Tree (Graph) – With points A and G –Fill in free spots (Configuration Space) –Try to link A to G Configuration Space Algorithms –Sampling-based Faster, less computing power –Combinatorial More complete

Configuration Space

DARPA Challenge Defense Advanced Research Projects Agency 2004 Desert Course 2005 Off-road, mountain terrain 2007 Urban Challenge –Collision Avoidance –Obey traffic signs

Stanley 2005 DARPA Challenge winner Volkswagen Touareg modified with onboard computers

Stanley’s Sensory 5 LIDAR lasers 24 GHz RADAR Stereo camera Single-lens camera

Path Analysis Built in RDDF (database of course) Vehicle predominantly followed the RDDF data

Obstacle Detection Machine Learning Approach Accuracy value of data is based on how human’s perform Slows down when a path can not be found quickly Grid of either occupied, free, or unknown spots

Issues with mapping scheme Errors in determining environment –12.6% of areas determined as obstacle was not

Alice out of challenge

Personal Opinions Good progress since the first challenge Not until the 2007 challenge will we really know if a fully autonomous vehicle is possible in the near future Other approaches more likely to be developed into mainstream before fully autonomous vehicles