Autonomous vehicles use sensors to detect their surroundings, gather data to determine the best actions to take, and deliver control functions to operate without a human driver. They rely on technologies like detection sensors, navigation systems, and motion planning algorithms to function autonomously. While control and navigation capabilities have advanced, challenges remain in fully sensing the environment, collision avoidance, and liability issues that must be addressed before autonomous vehicles can be used widely.

1. Autonomous Vehicles
By: Rotha Aing

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

3. 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

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

5. 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

6. 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

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

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

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

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

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

12. Navigation
• Already
available
• Combination of:
– GPS
– Roadside
database

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

14. 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

15. 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

16. “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

17. Configuration Space

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

19. Stanley
• 2005 DARPA
Challenge
winner
• Volkswagen
Touareg
modified with
onboard
computers

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

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

23. 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

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

26. Alice out of challenge

27. 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