#6 PRE-ASSIGNMENTS – CDIO ACADEMY 2017

PRE-ASSIGNMENTS – CDIO ACADEMY 2017

Feng Chia University, Hsin-Chien, Wang

11 June 2017

#6 – Social

I have interviewed above 10 people and almost up to 20 people through the net. They are my classmates, family and friends around the world, which not only live in the same country(Taiwan) with me but also live in Canada, China, Hong Kong and United State, and their ages range from 16 to 30.

 

Would you be interested in having an autonomous vehicle?

Do you drive a vehicle?

  Yes No

Yes

6

4

No 8

1

(Total interviewer: 19 people)

Take a view at the result we can find that people who do not drive vehicles seldom uninteresting in autonomous vehicles. We also discovered that people who are disability to drive vehicles really eager to own the freedom of transportation in individual way. And also, there are more people who drive vehicles would interesting in having an autonomous vehicle, mostly because they think autonomous vehicles can make driving much more easier. Secondly, some of them think they can make good use of time if they don’t need to spend time on driving and it might brings convenience to human. People don’t have any interesting in having an autonomous vehicle whether they could drive vehicles or not think vehicles should represent the person whose driving and it is too dangerous to get on vehicles which are unable control by human and it seems like autonomous vehicles are easy to be hacked after watching “fast and furious 8”. But their is also a person said that when he is disability to control any vehicles he would try to rely on it.

This is the most interesting pre-assignments over the weeks, through the interviews we can find out what impression did autonomous vehicles have made in everyone’s mind. I ride my scooter everyday to school, because scooter is the most convenient way to go anywhere in my city(Taiwan, Taichung). And I would be interesting if I have a chance having an autonomous vehicle, because of the bad weather like instant heavy rain in this season, I think autonomous vehicles could solve traffic jam and parking problem in the city and reduce the traffic accidents happening.

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#5 PRE-ASSIGNMENTS – CDIO ACADEMY 2017

PRE-ASSIGNMENTS – CDIO ACADEMY 2017

Feng Chia University, Hsin-Chien, Wang

28 May 2017

#5 – Law

Suppose that Carlos sues Alice, Bob, the AV manufacturer, the AV and the regulator of AVs for damages.

On September 2016, the National Highway Traffic Safety Administration released a policy report to accelerate the adoption of autonomous car technology (or HAVs, highly automated vehicles) and provide guidelines for an initial regulatory framework. Summary of key points is provided below:

• States are responsible for determining liability rules for HAVs. States should consider how to allocate liability among HAV owners, operators, passengers, manufacturers, and others when a crash occurs.
• Determination of who or what is the “driver” of an HAV in a given circumstance does not necessarily determine liability for crashes involving that HAV.
• Rules and laws allocating tort liability could have a significant effect on both consumer acceptance of HAVs and their rate of deployment. Such rules also could have a substantial effect on the level and incidence of automobile liability insurance costs in jurisdictions in which HAVs operate.
• In the future, the States may identify additional liability issues and seek to develop consistent solutions. It may be desirable to create a commission to study liability and insurance issues and make recommendations to the States.

In this cases, autonomous car manufacturers are compelled to reduce the danger of their products as much as they can within a reasonable cost structure. Yet, strict liability covers an expansive range of potential harms that manufacturers may find difficult to protect against. Manufacturers may then find themselves incentivized to pass on potential costs of liability to consumers through higher prices.
Furthermore, product liability cases distinguish among various types of defects.Carmakers should take liability for any system in the car.

Reference:

  1. Kelly, Heather (30 October 2012). “Self-driving cars now legal in California”. CNN. Retrieved 11 October 2013.
  2. Autonomous vehicles: The legal landscape in the US: http://www.nortonrosefulbright.com/knowledge/publications/141954/autonomous-vehicles-the-legal-landscape-in-the-us
  3. http://www.quinnemanuel.com/the-firm/news-events/article-january-2016-legal-issues-raised-by-the-driverless-vehicle-revolution-part-2/

#4 PRE-ASSIGNMENTS – CDIO ACADEMY 2017

PRE-ASSIGNMENTS – CDIO ACADEMY 2017

Feng Chia University, Hsin-Chien, Wang

28 May 2017

#4 – Personality and Teamwork
My name is Hsin-Chien and I majored in transportation technology and management. My friends said that I’m a easy going and warmhearted person. During almost three years in Feng Chia University, I’ve being a tug-of-war player, a pitcher in softball team, and a ground staff in mandarin airlines. I consider myself a team player, that’s the reason why I’ve developed a passion for this program. I’m very excited to explore the unlimited potential of autonomous car and see how my imagination and cooperation will benefit the team.

#3 PRE-ASSIGNMENTS – CDIO ACADEMY 2017

PRE-ASSIGNMENTS – CDIO ACADEMY 2017

Feng Chia University, Hsin-Chien, Wang

21 May 2017

#3 – Ethical

Alice buys an autonomous vehicle from Bob, who sells them. The autonomous vehicle has different settings, some more aggressive (where the autonomous vehicle drives faster and brakes harder), and some less. Alice sets the autonomous vehicle to its most aggressive setting. One night on a dark and wet road, Alice hits a pedestrian, Carlos, who was jaywalking. Carlos is badly hurt.

In this situation, we could have three simple assumptions:
1. If the right of way only for autonomous vehicles, the responsible for Carlos’ injuries are definitely himself, because the way he acted had made himself badly hurt, and we could consider his behavior as the incidence of suicide.

2. If the right of way for pedestrians and non-autonomous vehicles, and also forbidded drivers switch on auto-pilot mode, the manufacturers or the owners would have responsibility for this accident. Then we could focus on who took the autonomous vehicle in this road, the autonomous vehicle system or the driver.

3. If the right of way wasn’t clearly enough, the regulators might have the responsibility for this accident. Because of the unclearly traffic rules reducing the alertness of drivers and pedestrians.

Due to autonomous vehicles getting ready to drive on road, suitable transportation regulation measures, safety education and related propaganda need to be established, especially for dealing with shared right of way as well as for the confrontations that occur at intersections. Right-of-way rules help autonomous vehicles drive safely. These rules go along with courtesy and common sense. Autonomous vehicles, non-autonomous vehicles, and pedestrians must follow these rules, too. And also, the manufacturers and autonomous vehicles owners must understand how would the autonomous vehicle work when the emergency situation were happened.

“When the great way prevails, the world community is equally shared by all.”-THE CHAPTER OF GREAT HARMONY

#2 PRE-ASSIGNMENTS – CDIO ACADEMY 2017

PRE-ASSIGNMENTS – CDIO ACADEMY 2017

Feng Chia University, Hsin-Chien, Wang

12 May 2017

#2 – Technological

AUTONOMOUS VEHICLE TECHNOLOGIES

Figure_2_Autonomous Vehicle Technologies.jpg

Resources: http://www.css.snre.umich.edu/sites/default/files/Autonomous_Vehicles_Factsheet_CSS16-18.pdf

 

RARDAR APPLICATION IN AUTONOMOUS VEHICLE

  • How is it currently being used?
  1. lane-change assistanse
  2. blind-spot detection
  3. self-impact
  4. cross-traffic alert
  5. brake-assistance/ collision avoidance
  6. adaptive cruise control
  • What are its benefits and drawbacks?

Benefits of radar:

  1. The radar can see through the medium consisting of fog, snow, rain, darkness, clouds etc.
  2. Radar signal can penetrate and see through insulators.
  3. It can help find out following parameters of object or target:
  • Range
  • Angular Position
  • Location of Target
  • Velocity of Target
  1. It can distinguish fixed as well as moving target types.

Drawbacks of Radar:

  1. It can not distinguish and resolve multiple targets which are very close like our eye.
  2. It can not recognize color of the targets.
  3. It can not see targets which are in the water and are too deep.
  4. It can not see targets which are placed behind some conducting sheets.
  5. It is also difficult to recognize short range target types.
  6. Switching time of radar duplexer is very crucial when targets are very close. In this situation reflected pulses arrive much earlier than the time required to connect receiver part with the antenna by the duplexer. This results into “reflected pulse is not received by the radar”.
  • What is its future potential?

3D mapping of the traffic situation

In terms of further challenges, 3D mapping for autonomous driving in future will be the next level. There are two main ways to acquire data for HD maps in 3D. The first way involves instrumented, high-tech equipped vehicles for efficient and accurate 3D mapping of roads. The second way involves unmanned aerial vehicle systems equipped with similar lightweight sensors such as LiDAR or stereo cameras, providing a novel platform for photogrammetry. This is where LiDAR seems destined to prove advantageous. LiDAR works according to the same principle as radar and is based on measurement of the reflection of a transmitted signal. While radar relies on radio waves, LiDAR makes uses of light beams (e.g. laser). The distance to the object or surface is calculated by measuring the time that elapses between the transmission of a pulse and when a reflection of that pulse is received. The big advantage of LiDAR is that the technology enables much smaller objects to be detected than is possible with radar. In contrast to a camera, which views its environment in focal planes, LiDAR delivers an accurate, relatively detailed 3D rendering. This allows autonomous fast data acquisition, low errors, and dense point clouds for the realization of precise 3D maps. Once autonomous driving is implemented, HD maps in 3D will become essential.

Proximity Detection 340x340

Resources: https://www.melexis.com/en/insights/knowhow/how-sensor-technology-shape-cars-future

 

  • References:
  1. http://www.css.snre.umich.edu/factsheets/autonomous-vehicles-factsheet
  2. http://big5.xinhuanet.com/gate/big5/news.xinhuanet.com/auto/2012-12/04/c_124046607.htm
  3. http://www.rfwireless-world.com/Terminology/Advantages-and-Disadvantages-of-Radar.html
  4. https://www.melexis.com/en/insights/knowhow/how-sensor-technology-shape-cars-future
  5. http://www.sciencedirect.com/science/article/pii/S2095809916309432

#1 PRE-ASSIGNMENTS – CDIO ACADEMY 2017

 

PRE-ASSIGNMENTS – CDIO ACADEMY 2017

Feng Chia University, Hsin-Chien, Wang

06 May 2017

#1 – Introduction

Briefly describe SAE automated vehicle classifications:

  • Level 0 – No automation:

Automated system has no vehicle control, but may issue warnings.

  • Level 1 – Driver Assistance:

More than one kind of Driver Assistance systems in the vehicle, for instance, adaptive cruise control (ACC), Parking Assist with automated steering and Lane Keeping Assist (LKA).

  • Level 2 – Partial Automation:

The automated system executes accelerating, braking, and steering. The automated system can deactivate immediately upon takeover by the driver.

  • Level 3 – Conditional Automation:

The system including monitoring of the driving environment, under certain conditions (e.g. traffic jams on motorways) and needs to alert the driver in advance if conditions require transition to driver control.

  • Level 4 – High Automation:

The automated system can control the vehicle in all but a few environments such as severe weather. The driver must enable the automated system only when it is safe to do so.

  • Level 5 – Full Automation:

The automatic system can drive to any location where it is legal to drive.

 

NAVYA ARMA Shuttle

navya

NAVYA introduces its fully autonomous NAVYA ARMA Shuttle to the U.S. market at Michigan City, and the University of Michigan’s simulated city for testing autonomous vehicles. This is one of the most prestigious testing grounds in the world and is managed by the University of Michigan. The NAVYA ARMA is it the only European shuttle bus on the site.

The shuttle has been designed to ensure the specific functions of an autonomous vehicle whilst optimizing on navigation and safety. It is equipped with a multitude of sensor technology to provide 3D vision which enables it to map out the environment, detect obstacles in its path and identify traffic and road signs. The NAVYA ARMA shuttle can reach speeds of 45 km/hr.

The shuttle bus is the result of 10 years’ R&D experience that has enabled it to gain the highest level of autonomy possible, making it a fully autonomous and driverless production vehicle.

 

Olli

olli

Olli is a 3D-printed self-driving electric shuttle bus designed by the online community and built by Local Motors. Designed to streamline shared transportation systems around the world, this self-driving car could be the answer to public transportation issues.

“The Olli system is responsible for all aspects of vehicle operation: localization, object detection, steering, acceleration/braking, emergency/safety sequence, and so on,” said Local Motors Product Manager Jonathan Garrett.

The vehicles have already been tested in Maryland and avoided accidents during trials, despite facing additional stress tests from pranksters.

Olli is Level 4 and uses LIDAR for its “vision” unit versus the forward-facing cameras and radar. Local Motors has suggested the use of Olli to fill gaps in city transit systems, transporting students across campuses, or even transporting employees across corporate grounds. Taking self-driving vehicles to the next level, Olli is ready to get to work, as soon as regulations allow it.

 

4 possible applications for autonomous vehicles

  1. The autonomous end-to-end supply chain

The so-called last-mile, the delivery to the doors of businesses and consumers, is probably the most complex task in the supply chain. The automated delivery vehicles could take this task easily.

  1. Personal Rapid Transit: https://youtu.be/5G9X0voSi2Y

PRT is designed as an extension of existing pod cars and serves as a subway car on demand, and is used in areas where the demand for transport systems is irregular and high during peak hours.

  1. Driverless intercity bus and autonomous freight transport

This application could reduce the cost of road haulage costs, margins and profits and the disadvantage of human factor, and also solve the driver shortage crisis.

  1. Non-stopped autonomous bus system

This system is combined with one main autonomous bus and one autonomous shuttle. People can get on the main bus easily by taking the shuttle in the bus stop, and then the shuttle will connect to the main bus when it pass by. Another shuttle, which is connected at the previous stop will separate itself from the main bus to the next stop. It’s a pattern which can greater efficiency of public transportation because the main bus just keep going from first stop to last stop.

 

References

  1. AdaptIVe system classification and glossary on Automated driving(PDF).
  2. http://www.sae.org/misc/pdfs/automated_driving.pdf
  3. http://navya.tech/wp-content/uploads/2016/12/NAVYA_MCITY_PRESS_RELEASE.pdf
  4. https://localmotors.com/olli/
  5. Wolfgang, Lehmacher (01 Mar 2017) “How automated delivery vehicles will transform your city?”
  6. https://youtu.be/5G9X0voSi2Y