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
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