ROS Insights for Self-Driving Cars

•

0 likes•157 views

This document discusses different approaches to decision making for self-driving cars, including state machines, reinforcement learning, and relational learning. It describes 5 levels of vehicle automation from no automation to full automation. For decision making, level 1 uses state machines with preprogrammed rules, level 2 adds reinforcement learning to accumulate knowledge of rewarding actions, and level 3 uses relational learning and cognitive prediction. Examples are given of using these approaches for tasks like controlling car lights and navigation.

Technology

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

2
www.linkedin.com/in/brjapon
brjapon@therobotacademy.com
IO Social Robot JUS Open Source Robot
1995...
2015 2017 2020

3
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

1Robot technologies applied
to Self Driving Cars

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

6
Level 1 - No Automation
Level 2 - Partial Automation
Assist in controlling speed and steering:
stop-and-go traffic & centering in the lane

7
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

8
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

9
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

11
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

12
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

3Decision making II:
Reinforcement Learning

14
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

15
Level 2: Reinforcement Machine case study
Learning optimum paths
… without any prior knowledge of the environment
State
Action
Reward

16
State characterization is based on robot perception
LSD Laser Distance Sensor

3Decision making III:
Relational Learning

19
experience
Relational
learning
Stimulus-
Response
Cognitive
prediction
Level 3: Relational learning
Decision making III
Reinforcement
learning
State machine
(no learning)

20
Level 3: Relational Learning case study
AGV vehicles plus SLAM

21
Feature 1: Use computer vision to follow the lane

23
Feature 2: Simultaneous Localization and Mapping (SLAM)
● Build a map of the environment

24
● Driving along a planned trajectory using navigation (path A)

25
● Driving along a planned trajectory using navigation (path B)

The document discusses different levels of automation for vehicles, from no automation where the driver performs all tasks to full automation where the system controls the vehicle under all conditions. It also examines how this classification framework could apply to AI-driven software development and how tools like low-code and AI coders could impact team structures and the role of software developers. The framework aims to effectively manage complexity and translate real-world problems into digital models.

Autonomous cars

Anmol Parimoo

Robot cars

Raghava Lagudu

This document provides a summary of a term paper on robot cars. It includes an abstract, introduction, and sections on the evolution of robot cars from early experiments in the 1970s and 1980s to more advanced testing by companies like Google. A key focus is on collision avoidance techniques, which are seen as an important factor in the development of self-driving vehicles given statistics on accidents. The paper outlines collision avoidance systems used by Ford and electronic stability control systems.

GovTech Explainer: Self-Driving Cars

Dustin Haisler

The document discusses self-driving cars and their levels of automation from level 0, where the driver controls the vehicle at all times, to level 4 where the vehicle can perform all driving functions without human intervention. Currently most vehicles are at level 2 with some automated features but require driver attention. The document also discusses the regulatory status of self-driving cars and potential impacts on government revenues from taxes and need to revisit transportation infrastructure plans.

Ieeepro techno solutions ieee 2014 embedded project gps-copilot real-time ...

srinivasanece7

LATEST IEEE PROJECTS, 2016 IEEE PROJECTS, IEEE Projects in Chennai, Engineering Projects, Engineering IEEE Projects, Final Year Projects, Final Year IEEE Projects, IEEE Projects 2015 - 2016. 2016 IEEE projects, 2016 IEEE java projects, 2016 IEEE Dotnet projects, 2016 IEEE .net projects,IEEE Projects, IEEE Projects 2016 ,IEEE Academic Projects, IEEE 2016 Projects, IEEE, IEEE Projects, IEEE Software Projects, Latest IEEE Projects,IEEE Student Projects, IEEE Final year Student Projects,Final Year Projects, final year IEEE 2016 projects, final year 2016 projects,ENGINEERING PROJECTS, MCA projects, BE projects, Embedded Projects, JAVA projects, J2EE projects, .NET projects, Students projects,BE projects, B.Tech. projects, ME projects, M.Tech. projects, M.Phil Projects.

Drive less cars

AbhinavKumarDubey1

An autonomous car, also known as a self-driving car, uses sensors and computer vision to navigate and drive with little to no human input. Autonomous cars use radar, lidar, sonar, GPS and computer vision to perceive their surroundings and an advanced control system to identify navigation paths and obstacles. There are six levels of vehicle automation from fully manual to fully autonomous without human intervention. Autonomous vehicles have advantages like reducing accidents, easing traffic, and stress-free parking, but also disadvantages like high costs, safety and security concerns, and sensors being prone to failure or hacking.

Semi-Autonomous Vehicle

IRJET Journal

The document describes a semi-autonomous vehicle system using a Raspberry Pi and Arduino. The Raspberry Pi uses computer vision algorithms and a camera to detect lanes and traffic lights. It communicates via serial with an Arduino, which controls a motor driver and motors to move the vehicle. The goal is to allow the vehicle to drive itself while following traffic rules, responding to lights and avoiding collisions. The system is intended to help reduce accidents by taking over some driving tasks from human drivers.

Career Opportunities in Electric vehicles and Autonomous vehicles .pptx

Murali krishna U

The document discusses career opportunities in electrical engineering. It outlines opportunities in various industries such as automotive, electric vehicles, and autonomous vehicles. Some key roles mentioned include electric motor design, battery management systems, vehicle integration, power electronics, and software development for autonomous functions. The document provides online resources for learning more about these topics on platforms like Udemy and YouTube. It emphasizes the need for interdisciplinary skills to succeed in future roles and encourages starting to learn relevant technologies.

Driver-less cars by avaneesh shuka

avaneesh shukla

Driverless cars use automatic control systems to navigate roadways without human assistance. Sensors detect road conditions and obstacles while navigation systems plot routes. Motion planning software controls acceleration, braking, and steering to maneuver safely through traffic. Early technologies like ABS, cruise control, and automated guided vehicles demonstrated autonomous vehicle components. The 2007 DARPA Urban Challenge advanced driverless car capabilities by having vehicles navigate a 96km urban course obeying all traffic laws. While autonomous vehicles can increase road safety and capacity, cybersecurity risks and sensor failures present disadvantages requiring further development.

AUTONOMOUS VEHICLES 2.pdf

Javierwilsongrillo

Ml4pl generating

Nicolás Cardozo Alvarez

This document discusses using machine learning to generate software adaptations. It presents an initial prototype of an adaptation engine that learns from previous user actions and sensor data to automatically adapt software behavior. The engine uses an unsupervised learning approach to analyze historical action and state data and generate adaptation rules. This learns adaptations on the fly rather than requiring developers to manually specify all possibilities. However, open issues remain around automating the learning process, integrating new sensors, and providing guarantees that adaptations will maintain safe behavior. The use of machine learning could also increase long-term software maintenance costs.

Generating software adaptations using machine learning

Universidad de los Andes

Talk presented at the 2018 machine learning for programming languages workshop colocated with ECOOP in Amsterdam. This work presents the use of reinforcement learning for the discovery of context objects to drive behavioral adaptations. Our technique uses information gathered from the systems environment to define context, associating the actions taken by the user in such situations to define and automate behavioral adaptations

Automated Vehicle (Google Car)

sohaildanish

Long Distance Teleoperation for Trucking

Jonathan Hujsak

This document discusses the potential for autonomous long-haul trucking and outlines several key points: 1) Autonomous trucking could help address issues like driver fatigue, shortages of drivers, and safety issues that cause over 100,000 injuries annually from truck crashes. 2) The technology for autonomous trucks is advancing, with systems reaching Level 3 automation where drivers are not required for extended periods but must be ready to intervene. 3) Fully autonomous Level 4 trucks are the ultimate goal, allowing trucks to drive themselves without any human intervention needed. 4) Remote monitoring and control centers could help manage fleets of autonomous trucks and ensure safety. Standards from other industries will help develop reliable autonomous software.

How to take over the world with artificial intelligence final

Sergey Shelpuk

The document outlines the three main steps needed to take over the world with artificial intelligence: 1) Perception - using techniques like Gaussian filters and pattern recognition to understand the state of the world. 2) Planning - deciding on a course of action using searches and Markov decision processes. 3) Control - executing the plan using controllers like PID and dealing with randomness. It provides examples of how these steps are implemented in applications like self-driving cars, robot helicopters, and optimizing data center resource utilization to reduce costs.

chapter2-(Intelligent Agents).ppt

ssuser99ca78

The document discusses intelligent agents and their environments. It defines key concepts like agents, environments, rational agents, and the PEAS (Performance measure, Environment, Actuators, Sensors) framework for defining agents and their settings. It also categorizes different types of environments based on their observability, determinism, episodic versus sequential nature, static versus dynamic properties, discrete versus continuous properties, and single versus multi-agent aspects. Finally, it outlines the main types of agents as simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.

Automated mobility and more lv lions - 29 dec16

Douglas Bodde

1. The document discusses how artificial intelligence and deep learning will be key technologies enabling automated vehicles. Deep learning may lead to a winner-take-all market where the first company to solve challenges like interpreting ambiguous situations takes the entire market. 2. As mobility shifts to a shared, on-demand model enabled by self-driving cars, the traditional auto industry will likely decline steeply within 10 years. New network models of collaboration between companies may replace traditional internal research and development approaches. 3. Mobility will be delivered increasingly as a service through ride-sharing and fewer privately-owned vehicles, which will require only 20-30% of the current vehicle fleet in urban areas.

Driverless cars

Vandana jamnal

Self-driving cars have been experimented with since the 1920s but the first truly autonomous car appeared in 1980. The National Highway Traffic Safety Administration and the Society of Automotive Engineers have developed classification systems for levels of vehicle automation from no automation to full automation. Fully autonomous vehicles can sense their environment using sensors like radar, GPS, and computer vision and navigate without human input. They use multiple processors and algorithms for tasks like image processing, LIDAR processing, radar processing, feature extraction, object classification, mapping and localization, and path planning. While the technology promises advantages, there are also potential disadvantages to address.

10 BM60064_MIS_RobotCars

Raghava Lagudu

1) Robocars are equipped with sensors, cameras, and AI to drive autonomously or assist drivers. Google cars have already driven over 1000 miles without human intervention. 2) Collision avoidance systems aim to reduce accidents and save lives by detecting hazards and controlling vehicles. Automatic braking and blind spot detection help avoid collisions. 3) While robot cars promise safety and mobility benefits, legal and accountability issues around autonomous vehicles remain challenging.

Term paper on robot cars

Raghava Lagudu

This document provides an overview of robot cars and collision avoidance systems. It discusses the evolution of robot cars from early experiments in the 1970s to current technologies being tested. Key aspects of robot cars discussed include using existing infrastructure, prioritizing safety, and the potential for fully autonomous vehicles. The document also examines collision avoidance systems used by Ford, including blind spot detection and stability control systems that use sensors and braking to help avoid accidents.

Backjin lee autonomous vehicle slideshare

Backjin Lee

The document summarizes the results of a survey on public acceptance and preferences of autonomous vehicles in Korea. The survey found that respondents were most concerned about safety issues from system failures but saw benefits in reduced stress and ability to do other activities while driving. They preferred higher level autonomous options like parking pilots over traffic jam assistants. Willingness to pay increased slightly for higher levels but connectivity features had little added value. This suggests autonomous vehicles need to prioritize safety and that a mix of levels will coexist as preferences differ.

Designing the f-HMI for Automated Vehicles Case Study

RojinMoghadam

This document describes the design of a future human-machine interface (HMI) for automated vehicles. It includes transitional journey maps showing different driving modes and scenarios. The HMI concept features a central console displaying driving mode, surrounding context, and notifications. Additional customizable panels could show apps, controls, or an action log. Scenarios demonstrate transitions between manual, cooperative, and autonomous driving while multitasking like teleconferencing, reading, or listening to music. The goal is to inform users about vehicle actions and upcoming transitions between automation levels.

2015-1-19

Jin Xie

The document discusses methods for predicting a vehicle's trajectory using fractal correlation dimension analysis and generative machine learning algorithms. It first introduces using correlation dimension to extract features from nonlinear trajectory data and predict if a vehicle will turn left or right based on the dimension value. It then covers using a generative learning algorithm based on Bayes' rule to calculate the posterior probability of trajectories given observed data and predict the trajectory with the highest probability. Future work is outlined, including improving the robustness of generative learning algorithms and acquiring lane localization data from images.

Self-Navigation Car using Reinforcement Learning

Dr. Amarjeet Singh

In this paper, a project is described which is a 2-D modelled version of a car that will learn how to drive itself. It will have to figure everything out on its own. In addition, to achieve that the simulator contains a car running simultaneously &can be controlled by different control algorithms - heuristic, reinforcement learning-based, etc. For each dynamic input, the Reinforcement- Learning modifies new patterns. Ultimately, Reinforcement Learning helps in maximizing the reward from every state. In this first Part, we will implement a Reinforcement-Learning model to build an AI for Self Driving Car. Project will be focusing on the brain of the car not any graphics. The car will detect obstacles and take basic actions. To make autonomous car or self-driving car a reality, some of the factors to be considered are human safety and quality of life.

Iniciacion al Prototipado IoT con NodeRED

ROS Insights for Self-Driving Cars

Recommended

Recommended

More Related Content

Similar to ROS Insights for Self-Driving Cars

Similar to ROS Insights for Self-Driving Cars (20)

More from Bernardo Ronquillo Japón

More from Bernardo Ronquillo Japón (20)

Recently uploaded

Recently uploaded (20)

ROS Insights for Self-Driving Cars