ROS Insights for Self-Driving Cars

1. Dive into Self-Driving Cars
technologies using ROS and
Open Source software
Humanoid Robotics, Artificial Intelligence
and Automation
21 May 2020
TheRobotAcademy.com

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www.linkedin.com/in/brjapon
brjapon@therobotacademy.com
IO Social Robot JUS Open Source Robot
1995...
2015 2017 2020

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INDEX
Self Driving Cars in context
Decision making 1:
State Machines
Decision making 2:
Reinforcement Learning
Decision making 3:
Relational Learning
1
2
3
4

4. 1Robot technologies applied
to Self Driving Cars

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The 5 levels of Autonomous Vehicles
Self Driving Cars in context
Level 1 - No Automation Driver keeps full control: ordinary cruise control

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Level 1 - No Automation
Level 2 - Partial Automation
Assist in controlling speed and steering:
stop-and-go traffic & centering in the lane

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Level 1 - No Automation
Level 2 - Partial Automation
Level 3 - Conditional Automation
Self-driving under ideal conditions and with limitations:
Off ideal -> driver is required to take over

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Level 1 - No Automation
Level 2 - Partial Automation
Level 3 - Conditional Automation
Level 4 - High Automation
Drive themselves without human interactions:
restricted to known use cases

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Level 1 - No Automation Driver keeps full control: ordinary cruise control
Level 2 - Partial Automation
Assist in controlling speed and steering:
stop-and-go traffic & centering in the lane
Level 3 - Conditional Automation
Self-driving under ideal conditions and with limitations:
Off ideal -> driver is required to take over
Level 4 - High Automation
Drive themselves without human interactions:
restricted to known use cases
Level 5 - Full Automation
True driverless cars:
No need for a steering wheel and pedals
The 5 levels of Autonomous Vehicles

10. 2Decision making I:
State Machines

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Stimulus- Response
State machine
(no learning)
rules are preprogrammed (hard coded)
Given a concrete set of values of state variables,
always produce the same action
Level 1: State Machines
Decision making I

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Level 1: State Machine case study
Car switch lights
● (trigger) If state is OFF, (action) then ON when
○ (detection) Natural lighting is reduced <=
■ (scenario) dusk till dawn || tunnel || fog
● (trigger) If state is ON, (action) then OFF when
○ (detection) There’s daylight <=
■ (scenario) daylight
○ (detection) Intense light source <=
■ (scenario) Lights from another car traveling in the opposite direction

13. 3Decision making II:
Reinforcement Learning

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experience
Stimulus-
Response
Reinforcement
learning
Accumulate knowledge of
most rewarding actions for
every state of the system
State machine
(no learning)
Level 2: State Machines
Decision making II
rules are selected as its usefulness is
checked (soft coded) by trial and error

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Level 2: Reinforcement Machine case study
Learning optimum paths
… without any prior knowledge of the environment
State
Action
Reward

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State characterization is based on robot perception
LSD Laser Distance Sensor

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Running the learning process

18. 3Decision making III:
Relational Learning

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experience
Relational
learning
Stimulus-
Response
Cognitive
prediction
Level 3: Relational learning
Decision making III
Reinforcement
learning
State machine
(no learning)

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Level 3: Relational Learning case study
AGV vehicles plus SLAM

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Feature 1: Use computer vision to follow the lane

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Feature 2: Simultaneous Localization and Mapping (SLAM)
● Build a map of the environment

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● Driving along a planned trajectory using navigation (path A)

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● Driving along a planned trajectory using navigation (path B)

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