Written by: Natasha Kowalskyj, Social Media Summer Work Study
Edited by: Isabella Blandisi-Van Hee, Project Coordinator for Applied Research

As previously reported, there are a multitude of challenges surrounding the integration of autonomous vehicles (AVs) into our daily lives, with one of the most paramount challenges being the security of data as well as protecting the information collected and received by AVs. According to Griffin (2018), there are substantial cybersecurity threats within AVs, as self-driving cars will, and have already proven to be, irresistible to hackers. The implications of cyber threats impacting the functionality of AVs is an issue that needs to be acknowledged (through strong security measures) before the full implementation of AVs on our roads.  Interestingly, when looking at public perception of AVs, consumers have noted cybersecurity as their primary concern (Hsu, 2017). A study found that participants were anxious about losing control of the vehicle [due to hacking] as well as access to driving patterns and locations leading to an invasion of privacy (Hsu, 2017; Sivak & Schoettle, 2017).With the above mentioned points in mind, this Tech Report will review the personal data and safety threats that are possible due to an AV’s connected network. This report will also discuss how these threats can be mitigated for the successful future of self-driving vehicles.

It is essential to first explore cyber threats, which have already occurred during the testing phases of AVs. There have been previous instances where the reliability of AVs have been compromised for malicious intent. For example, in 2015, two security researchers were able to remotely hack into a Jeep and successfully disable its digital system while it was on the highway (Griffin, 2018). A year later, hackers were able to steal 100 cars in Texas with simply the use of a computer in order to unlock and start the vehicles (Griffin, 2018). These examples showcase a major threat as there is the potential for hackers to reroute a vehicle to a particular location. Theoretically, it has also been argued that terrorists could manipulate an AV to drive an explosive device into a populated area when it comes to autonomous transport trucks carrying hazardous materials (Griffin, 2018). While these cases are certainly alarming, they present the reality of how AVs may be compromised with malicious intent and helped by other technology. The main cybersecurity threats lie within Electrical Control Units (ECUs) connected to an internal network, which helps the AV run seamlessly (Toews, 2016). Since AVs today are equipped with 100 ECUs, there is ample opportunity for a cyber-attack. If a hacker were to gain access to vulnerable ECUs, they would be able to take control of any critical functions of driving (Toews, 2016).

Researchers have established various types of AV cybersecurity attacks, which will now be presented (see Linkov, Zámečník, Havlíčková, & Pai, 2019). Beginning with a “spoofing attack,” this is where the perpetrator uses a fake identity to potentially send false information about the vehicle’s location (see Linkov, Zámečník, Havlíčková, & Pai, 2019). Next, a “man-in-the-middle attack” is where a hacker intercepts the AV’s original communication and changes it—sending a completely different message back to the car.  A “denial of service attack” is where the hacker sends so much data to the AV that the communication signal is overloaded and cannot detect potential hazard warnings (see Linkov, Zámečník, Havlíčková, & Pai, 2019). A “jamming attack” is where radio noise is used to block the frequency used for vehicle-to-driver communication. Finally, a “black hole attack” would block communication without informing the AV about any missing messages (see Linkov, Zámečník, Havlíčková, & Pai, 2019). Other AV attacks could include falsified digital signatures of the driver, forcing the vehicle to restart, etc. (see Linkov, Zámečník, Havlíčková, & Pai, 2019).

With these threats looming, automotive manufacturers have not taken such scenarios lightly and have all begun to either acquire and/or invest in companies whose main focus is cybersecurity. Yoni Heilbronn, the executive of Argus Cyber Security, believes the best way to understand how automotive cybersecurity works, is to visualize them as having several layers of defense (Toews, 2016). For example, defensive software can be put on individual ECUs, like the vehicles brakes and into the internal network, in order to examine all network communications as well as be able to detect any changes in vehicle behaviour; this reinforces the AV against attacks (Toews, 2016).

The National Highway Traffic Safety Administration has formulated the best practices for cybersecurity in connected or automated vehicles. As mentioned above, it is a layered approach to protection with five principal functions: identify, protect, detect, respond, and recover (Griffin, 2018). Notably, cloud-security has been developed to recognize and correct any potential cyber threats before even reaching the AV, while also sending over-the-air updates in real-time (Toews, 2016). Another fundamental necessity in maintaining security is ensuring all necessary parts of AVs are sourced from trusted suppliers (Toews, 2016). For instance, companies like Tesla, Fiat Chrysler, and GM have created “bug bounty” initiatives to reward those who find and report any security weaknesses within their vehicles’ software. This is imperative as it fortifies their future systems against vulnerabilities (Toews, 2016).

With all of these possibilities on the horizon, it is important to note that there are solutions in the works. Law enforcement teams are collaborating with manufacturers and lawmakers to ensure the needs and concerns of police are also considered in the development stages of AVs (Griffin, 2018). Another proposed solution is that companies managing AV cybersecurity ensure trust and responsibility among their employees (e.g., confidentiality) as well as carefully monitoring their employees for potential cases of abuse (see Linkov, Zámečník, Havlíčková, & Pai, 2019). Another level of proactive prevention is to understand the motivations and characteristics of AV attackers; their socialization to rule-breaking behaviour and further possibilities like psychopathy and narcissism (see Linkov, Zámečník, Havlíčková, & Pai, 2019). These methods may need to delve further into intelligence-based techniques such as analyzing online social network profiles in order to understand hackers’ political leaning as well as thoughts on major issues like terrorism, organized crime, and foreign governments (see Linkov, Zámečník, Havlíčková, & Pai, 2019). Ultimately, the goal is to ensure the safety of all citizens and communities by maintaining the highest level of security in all developed connected and autonomous vehicles.

 

References

Griffin, M. L. (2018). Steering (or not): Through the social and legal implications of autonomous vehicles. Business, Entrepreneurship & the Law, 11(1), 82-114.  Retrieved from https://digitalcommons.pepperdine.edu/jbel/vol11/iss1/4

Hsu, T. (2017, February 8). Cybersecurity concerns about autonomous vehicles spark consumer anxiety. Retrieved from https://www.trucks.com/2017/02/08/cybersecurity-autonomous-vehicles-concerns/

Linkov, V., Zámečník, P., Havlíčková, D., & Pai, C.W. (2019). Human factors in the cybersecurity of autonomous vehicles: Trends in current research. Frontiers in Psychology, 10. doi: 10.3389/fpsyg.2019.00995

Sivak, M. & Schoettle, B. (February 2017). Cybersecurity concerns with self-driving and conventional vehicles.University of Michigan Report. Retrieved from http://umich.edu/~umtriswt/PDF/SWT-2017-3.pdf

Toews, R. (2016, August 25). The biggest threat facing connected autonomous vehicles is cybersecurity. Retrieved from https://techcrunch.com/2016/08/25/the-biggest-threat-facing-connected-autonomous-vehicles-is-cybersecurity/.

Written by: Natasha Kowalskyj, Social Media Summer Work Study
Edited by: Isabella Blandisi-Van Hee, Project Coordinator for Applied Research

By now, these tech blogs have detailed the historical elements, which are necessary for an autonomous vehicle (AV) to function. This blog has also covered the AV implementations happening in Ontario, broken-down how this technology works and what this will mean for the province and Canada as whole, once cities become smarter. The projected benefits have also been explored: environmental, economic, safety, as well as increased accessibility, and what those will provide to the average citizen when cities begin implementing AVs into their future smart cities. These all brought the reader a sense of utopia when learning about AVs being the main mode of transportation and how it will benefit their day-to-day life. However, the reader should be informed about the challenges and necessary changes to everyday life, as they are set to be impactful for many reasons.

The question that first comes to mind with AV implementation is the level of safety the self-driving vehicle will have, not just for the human(s) in the car, but for every pedestrian around these vehicles. Google’s AV is a success story of live road testing. This AV successfully drove itself 800,000+ kilometers on public roads without a single accident (Hanna, 2015). However, not every test of a self-driving vehicle has had the same track record. A year ago, there was an incident where a self-driving Uber [with a human driver present in the car] was being tested on the roads of Tempe, Arizona. Its internal computer did not perceive a human wearing dark clothes crossing its path (with a bicycle) as an avoidable threat and unfortunately, this technical error resulted in the pedestrian being killed by the impact (Somerville, 2018).

Looking at this incident closely, there are a couple of individual faults associated. First and foremost, not to diminish the individual who was struck, but the conditions for which they were crossing the road, can be argued to be dangerous (e.g., dark, no crosswalk); there is the potential that even a human driver may have not had the reaction time to avoid an impact. Once the internal surveillance footage was reviewed, the human driver was accused of being distracted behind the wheel. This point is important because they were required to maintain alertness at all times in case the vehicle required human intervention—it was Level 3 AV or less (see our first post for more information on AV levels). This was the reasoning why the AV did not brake immediately when it sensed the bike—it required human intervention (Somerville, 2018). The accident report concluded that Uber had also disabled the emergency braking system of the AV in question (Somerville, 2018), which is why the car did not respond safely as it should have. It was also concluded that the driver was indeed distracted, only looking up and braking a second after hitting the pedestrian. The Tempe Police Detective, Michael McCormick, required the human driver’s Hulu records to determine when they were on their phone, as the charges of vehicular manslaughter may be laid (Somerville, 2018). This case leads us to consider a discussion surrounding the policing of AVs, determining who is at fault in the event of an accident (especially fatal incidents), and how to insure drivers and AVs alike.

There is also a “grey area” surrounding intoxicated individuals and whether they should receive a punishment for being in the driver’s seat of an AV, the same way one would be punished if they were driving a normal car under the influence. If AVs will be used as a means to reduce drinking and driving (akin to taking taxis or Uber), for example, this question requires a clear answer. Looking at a situation where someone is under the influence, and is sleeping in an AV on their way home, it can be argued that the vehicle is then responsible for maintaining all driving aspects, not the intoxicated passenger. As such, if AVs were to be used in this capacity, we would need to mandate a Level 4 and up AV to avoid incidences like the Arizona case. Then, the question becomes, if a Level 4 or 5 AV is involved in an accident, whose fault is it? Hanna (2015) argues that it is not pertinent to punish an intoxicated individual for being in the driver’s seat of an AV, as it will monetarily cost society, putting more strain on an already overwhelmed justice system while providing little net-benefits. Some States have intensified the testing of AVs and require the human to have a clean driving record—as it relates to driving under the influence—within the last ten years (Hanna, 2015). Ultimately, supporters of autonomous vehicles argue they will make our roads safer by eliminating driving fatalities related to drinking and driving (US Department of Transportation, 2016). If 90% of vehicles on [US] roads were autonomous, the number of accidents would fall from six million a year to 1.3 million—eliminating two-thirds of driving-related deaths (Thompson, 2016).

There are projected challenges when AVs are on the road full-time and how law enforcement will be able to police these vehicles safely. For example, hypothetically, say someone is pulled over for an autonomy inspection and the officer reminds them the roadway they are currently on is certified for Level 2 automation. The police officer would then proceed to ask the driver to show them what level of automation they were driving at before they were pulled over. This poses a potential challenge because if someone was driving at a fully automated level (e.g., 4/5), who is at fault if the vehicle is driving completely autonomously? This becomes a considerable undertaking due to the decisions involving how the level of autonomy for a roadway gets decided. Notably, the discussion of how this will be enforced requires attention as well. The issues of policing, not just the roadways, but also the drivers, as well as deciding who is at fault when there is an accident involving AVs (and potentially distracted drivers), is still a prominent topic in this field. Ending this discussion on a positive note, police believe the use of autonomous vehicles will allow officers to focus their resources/time on more pressing societal matters such as reducing criminal activity (Fink, 2014).

There will also be significant changes to the citywide infrastructure (already in place) and how it will need to be manufactured for AVs to be connected and seamless while driving. The infrastructure necessary to accommodate AVs is expected to be high-tech and impactful at best. However, going through this process of tailoring our cities for the adoption of AVs is expected to save at least 10% on fuel consumption through platooning (as discussed in a previous post), and could also possibly save on public health costs (Duarte & Ratti, 2018). For instance, these costs can be saved through the reduction of road accidents, reducing health problems related to smog/pollution as well as energy consumption (Duarte & Ratti, 2018). While the undertaking of creating new and improved city wide infrastructure for AVs will be disruptive and costly, the implementations of AVs are set to reduce road fatalities by 99% once majority of cars are connected and autonomous (Duarte & Ratti, 2018). These improvements are also set to reduce congestion and improve mobility for underserved populations; all expected to save $60 billion to the US economy alone (Duarte & Ratti, 2018).

Implementations of AVs allow city planners to rethink urban life and the surrounding infrastructure to avoid the pitfalls of poor city planning, and to use this opportunity as a catalyst for what a future city can look and act like. Once drivers are no longer bogged down with the task of commuting, there is the likelihood of more urban sprawl, and with that comes a slew of new issues not previously considered (Duarte & Ratti, 2018). These issues include, but are not limited to: more time travelling to work, leading to increased energy consumption. This may also increase emissions of pollutants (Duarte & Ratti, 2018). Indeed, there is the potential for city centers to become sparsely populated, less active with social interaction, and to have fewer investments into public transportation as it relies on population density to be economically feasible (Duarte & Ratti, 2018). These are only a few of the infrastructure issues expected to become a reality once AVs, and the necessary city transformations, are in fully place.

Like anything in society, the benefits and challenges of embarking on something new has steep learning curves. The projected pros and cons span across both ends of the “disruptive spectrum” and until the time has come to fluidly test these new ways of living connected to vehicles and cities around us, we will not fully know how true or false some of these benefits or challenges are. Until then, cities all around the world will be testing out various levels of AVs (e.g., shuttles, taxis) in order to explore their viability within our day-to-day lives for future generations.

References

Duarte, F., & Ratti, C. (2018). The impact of autonomous vehicles on cities: A review. Journal of Urban Technology, 25(4), 3-18. doi:10.1080/10630732.2018.1493883

Fink, D. (2014, June 22). Autonomous cars: Driving on auto pilot. Police: The Law Enforcement Magazine. Retrieved from https://www.policemag.com/341158/autonomous-cars-driving-on-auto-pilot

Hanna, K. (2015). Old laws, new tricks: Drunk driving and autonomous vehicles. Jurimetrics, 55(2), 275-289.

Somerville, H. (2018, June 22). Uber car’s ‘safety’ driver streamed TV show before fatal crash: Police. Retrieved from https://www.reuters.com/article/us-uber-selfdriving-crash/uber-driver-was-streaming-hulu-show-just-before-self-driving-car-crash-police-report-idUSKBN1JI0LB 

Thompson, C. (2016, December). 8 ways self-driving cars will drastically improve our lives. Retrieved from https://www.businessinsider.com/how-driverless-cars-will-change-lives-2016-12

US Department of Transportation (2016, September). Federal automated vehicles policy: Accelerating the next revolution in roadway safety [PDF file]. Retrieved from https://www.transportation.gov/sites/dot.gov/files/docs/AV%20policy%20guidance%20PDF.pdf

Written by: Natasha Kowalskyj, Social Media Summer Work Study
Edited by: Isabella Blandisi-Van Hee, Project Coordinator for Applied Research

It can be argued that autonomous implementations within society are not an option without some kind of “community readiness assessment” that dictates, on numerous levels, whether a city is ready for this technology or if major changes would be necessary first. There are many projected benefits to autonomous vehicles being implemented within every city. With that in mind, let’s go over the expected benefits and create a dialogue surrounding this topic. In particular, this post will cover benefits related to safety, increased accessibility, environmental benefits as well as economic benefits.

Safety is naturally the first question when talking about any autonomous vehicle (AV). How can we guarantee they will not be more dangerous on the roads? Well, the smartest and most comprehensive intelligence is already driving on the roads as the human brain is still superiorly intelligent. However, humans are inconsistent and not always reliable due to factors such as sleep deprivation, impaired driving, and/or distracted driving. As such, it seems we could benefit from artificial intelligence to help drivers stay safe. Statistics show that 86% of fatal crashes were due to distracted drivers and with the implementation of AVs, this number could be sufficiently lowered because of the driver-assist systems. These systems will aid a human driver with early hazard warnings and emergency braking when the car detects hazards, (at Level 4 of automation or higher) (Transport Canada, 2019a). Essentially, this will help human drivers behind the wheel (Level 3 or lower) to make better decisions and stay safer. In particular, Europe has adopted more AV systems through the newly implemented ‘truck platooning’ because of the benefits like increased road safety and the subsequent fuel efficiency—which will be discussed in more detail (Turnbull, 2017).

Another societal benefit of being part of a smart city with AVs, will be increased accessibility. Indeed, AVs will provide access and enhance the mobility for Canadians, especially those underserved in society. Low income families, seniors, populations living in rural areas and those with disabilities are just some of the populations who are regularly overlooked when it comes to public transportation. As such, the implementation of AVs aims to improve reliability, comfort, and convenience (Transport Canada, 2019a). Studies have found that a fully self-driving Level 4 AV will likely promote an increase in mobility within the elderly, disabled, and non-driving populations (Harper, Hendrickson, Mangones, & Samaras, 2016). Thus, AVs can allow these underserved populations to rely less on walking, limited public transit, or being chauffeured by friends or family members (Harper et al., 2016).

 In relation to the environmental benefits, they rely on environmental policies being put in place to help reduce road congestion and decrease fuel consumption and emissions (Transport Canada, 2019a). For example, through automation, major roads like highways and freeways would become streamlined if 18-wheelers were connected through the abovementioned “Platooning System.” To explain, commercial autonomous trucks can connect to whichever truck is leading the fleet and it ensures they all travel at the same speed until the internal map informs each autonomous truck to break away from the pack (e.g., due to perceived traffic jams) and get off at the next exit (Transport Canada, 2019b). This reduces congestion because highways will be less populated. When highways are less congested, truck drivers, and drivers in general, will ultimately spend less time idling while stuck in traffic—meaning there is less carbon emission polluting the air. This also means individuals will save money by refueling their vehicles less often (Transport Canada, 2019b).

Last but not least, the economic benefits are vast since AVs are projected to change the job market, help avoid costly accidents (as stated earlier), and even save on fuel costs as just mentioned. AVs are set to increase productivity and create new jobs as different sectors of society will see individuals re-tooling and retraining their skill sets to adapt to the new automated version of their old job (once AVs are implemented). According to Stacy (2017), “development, maintenance, and improvements to telematics and other programming for software running the [autonomous] vehicles, plus infrastructure required to run autonomous public transport, will need huge manpower [employees]” (para. 11).

Additionally, take the ever present sector of long haul trucking—they travel across multiple countries to deliver products, goods and services to the masses, and when fleet platooning comes into effect, those who used to drive these 18-wheelers will have to retrain their skill set to be able to adapt to an autonomous version of what they used to do. If and when the trucking industry becomes fully automated, it is projected to save approximately $300 billion in various costs (Staff, 2017). AVs could benefit several sectors of society such as mining, farming, forestry, digital technology, automotive manufacturing, and transportation services, where brand new jobs are created or pre-existing jobs are retrained to be able to continue in the same field (Transport Canada, 2019a). Most of these are the “idealistic” outcomes of how society will change once everything becomes either autonomous or automated—until we are fully autonomous and there are smart cities everywhere, we will not be able to fully comprehend how many benefits are actually possible. However, the expectancy of what AVs can provide for average the Canadian is certainly promising.

References

Harper, C. D., Hendrickson, C. T., Mangones, S., & Samaras, C. (2016). Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions. Transportation Research Part C: Emerging Technologies, 72, 1-9. doi:10.1016/j.trc.2016.09.003

Stacy, P. (2017). Driverless cars will create new jobs, not destroy them. Lexis Nexis. Retrieved from https://blogs.lexisnexis.com/insurance-insights/2016/09/driverless-cars-will-create-new-jobs-not-destroy/

Staff, H. (2017, November 21). Report: Autonomous vehicles could save trucking $300 billion in labor costs. Retrieved from https://www.truckinginfo.com/143139/report-autonomous-vehicles-could-save-trucking-300-billion-in-labor-costs

Transport Canada. (2019a, February 15). Automated and connected vehicles 101. Retrieved from https://www.tc.gc.ca/en/services/road/innovative-technologies/automated-connected-vehicles/av-cv-101.html

Transport Canada (2019b, March 7). Cooperative truck platooning: Transport Canada’s innovation centre testing new trucking technologies to reduce emissions and improve safety. Retrieved from https://www.tc.gc.ca/eng/cooperative-truck-platooning.html
Turnbull, E. C. (2017). Hours of service of drivers: Harnessing autonomous technology for safer operations. Jurimetrics, 58(1), 105-125.

Written by: Natasha Kowalskyj, Social Media Summer Work Study
Edited by: Isabella Blandisi-Van Hee, Project Coordinator for Applied Research

Autonomous vehicles rely on a variety of sensors, like cameras, lidar, radar, sonar, a Global Positioning System (GPS), an inertial measurement unit (IMU), and wheel odometry (Drndarević, Jovičić, & Kocić, 2018). The first, most pivotal type of sensor to be used within an autonomous vehicle is a camera, which, at a very basic level, allows the vehicle to visualize its surroundings (Drndarević et al., 2018). Cameras are widely available and they are efficient at identifying texture interpretation. The application of cameras are endless and they are generally more affordable than radar or lidar, however the multi-megabytes of storage needed to process what the vehicle is seeing is presently, a bit of a hurdle (Drndarević et al., 2018).

Light Detecting and Ranging or “lidar” uses an infrared laser beam to analyze the distance from the sensor to a nearby object, with most current lidar using a rotating swivel to scan the laser beam across and pulse them to reflect off objects to then represent the object to the lidar (Drndarević et al., 2018). Generally speaking, the higher the resolution, the longer and better the wavelength, then the more the autonomous vehicle (AV) is able to visualize in rain and fog conditions (Drndarević et al., 2018). With lidar mapping of both static and moving environments, lidar is extremely necessary for AV and are therefore, fairly expensive right now. However, the market is seemingly moving in the way of down-sizing the size and cost of these sensors and they should be more affordable in the near future (Drndarević et al., 2018).

Next, Radio Detection and Ranging or “radar,” is a mature technological component within an AV that is sensor-integrated for capabilities of adaptive cruise control, blind spot detection, collision warning and avoidance while constantly being improved upon for different applications of autonomous driving (Drndarević et al., 2018). Compared to cameras and lidars, radars have a low-resolution and lower processing speeds for handling data output, but radar can be utilized for localization and generating radar environment maps. This permits objects to be detected which would not be visible otherwise and in terms of all the sensors in an AV, radar is the least affected by rain or fog (Drndarević et al., 2018).

Ontario being labelled as a front runner for the AV innovation began when the province became the first jurisdiction in Canada to take part in the 10-year autonomous vehicle pilot project, which started in 2016. Specifically, this pilot project allowed the on-road testing of AVs (The Canadian Press, 2019). Since then, the province has supported the establishment of the Autonomous Vehicle Innovation Network (AVIN) to grow industry-led research and development, and there has since been significant initiatives coming from small towns in Ontario (Autonomous Vehicle Innovation Network (AVIN), 2019). Take Stratford for example, it was one of the first cities to be transformed into an AV demonstration zone and smart city. AVs are seamlessly tested amongst the technological grid, which is being made available within Stratford’s downtown core (980 CFPL Staff, 2017). Testing of other automations has been constant within the tech-belt of Waterloo, and even in Quebec, where the system of long haul truck platooning has taken off. This fairly new concept allows 18-wheelers to stay connected on the road, with the front vehicle controlling the speed of any other connected vehicles behind it (Transport Canada, 2019b). Importantly, AV- innovations like this are projected to be safer as well as economically and environmentally beneficial for any city striving to be smarter (Transport Canada, 2019b).

While Stratford is not far from those residing in the Greater Toronto Area, there are a few up-and- coming innovations right here in the Durham Region, and specifically for the Town of Whitby that are worth mentioning. It has been announced that in 2019 the town will oversee the testing of the first autonomous shuttle bus. The autonomous shuttle will drive North and South up/down one street within the municipality, testing the accuracy and legitimacy of implementing AVs as well as what benefits it holds for the city (SmartCone Technologies, Inc., 2019a). The President of Durham College, Don Lovisa, supports this innovation saying: “…establishing an autonomous shuttle in the Town of Whitby will provide invaluable information about how this type of vehicle performs and operates in a safe, real-world environment and how it can benefit our local communities” (SmartCone Technologies, Inc., 2019a, para. 9).

Along with the implementation of this shuttle to Whitby, a new technology is set to accompany the shuttle, making the city ubiquitously “smart.” TheSmartCone technology is a modular Internet of Things (IoT) platform that will function as an extra set of eyes not just for AVs, but vulnerable road users as well, to provide confidence and comfort to all (SmartCone Technologies, Inc., 2019a). It has been used in securing dangerous work sites, controlling bicycle lane traffic, managing vehicle fleets and monitoring traffic incident scenes, crowd control and security surveillance (SmartCone Technologies, Inc., 2019b).  Specifically, SmartCone Technologies are set to make first responders, cyclists, and construction workers safer, while looking like a standard orange pylon (Lord, 2017). Built in Ottawa, the SmartCone is uniquely loaded with video cameras and motion detectors that are able to detect invisible threats like seismic variations, severe wind speeds as well as toxic gasses like carbon monoxide, sulfur dioxide, ozone and nitrous oxide and particulate matter (SmartCone Technologies, Inc., 2019b). Also, this technology will have the ability to read license plates and recognize faces, which is then sent to a live remote control room, where other capabilities like lidar are located. The purpose of this technology is to create an invisible technological platform for AVs in order to immediately collect data and use accordingly; this technology will not just alert the AV to dangerous changes, but will also allow the city to be connected into the forum that the SmartCone has compiled.

With innovations like these within Canada, and localized throughout Ontario in different capacities, it is important to go “back to basics” and clarify what some of the new and necessary terminology means.  It is also imperative to break-down what AVs consist of and how they work. In sum, this was a brief overview into the inner workings of the complex and highly anticipated AVs of the near future. Most of the abovementioned technologies have already been utilized in society. For instance, driving using automation like cruise control was first invented 70 years ago and has flourished to be one of the lesser automated features within vehicles today. More recently, alerts with backup cameras and sensors, have aided drivers in reverse parking or perfecting the parallel-park (in some cases, even hands free). Self-driving vehicles are now becoming more sophisticated and within the next decade, they may be available to all.

References

980 CFPL Staff (2017, November 8). Premier Wynne set to announce funding for self-driving car research in Stratford. Retrieved from https://globalnews.ca/news/3849728/wynne-funding-
self-driving-car-stratford/

Autonomous Vehicle Innovation Network (AVIN). (2019). Retrieved from https://www.avinhub.ca/

The Canadian Press (2019, January 23). Transportation minister announces driverless cars allowed on Ontario roads. Retrieved from https://globalnews.ca/news/4878814/driverless_cars_allowed_ontario_roads/

Drndarević, V., Jovičić, N., & Kocić, J. (2018, November). Sensors and sensor fusion in autonomous vehicles. Paper presented at the 26th Telecommunications Forum (TELFOR), Belgrade, Serbia. doi # 10.1109/TELFOR.2018.8612054

Lord, C. (2017, December 11). Made in Ottawa: SmartCone adds safety to the connected city. Retrieved from https://obj.ca/article/made-ottawa-smartcone-adds-safety-connected-city 

SmartCone Technologies, Inc. (2019a, May 16). SmartCone Technologies’ “SAFETY FIRST” autonomous shuttle solution coming to Whitby, Ontario. Retrieved from https://www.issuewire.com/smartcone-technologies-safety-first-autonomous-shuttle-solution-coming-to-whitby-ontario-1633736884658862

Written by: Natasha Kowalskyj, Social Media Summer Work Study
Edited by: Isabella Blandisi-Van Hee, Project Coordinator for Applied Research

Before the 21^st century, the technology we are seeing today within an autonomous vehicle (AV), which allows the AV to fluidly operate, were long created, tested, and used in combat/defense (Encyclopedia Britannica, 2019). For example, sonar technology was first utilized as a means of detecting icebergs, but piqued interest during World War I with the threat of submarine warfare. As such, by 1916, a passive system of sonar consisting of towed lines and microphones, was developed and used to detect submarines (Encyclopedia Britannica, 2019). It was used strictly for underwater detection and, while there are autonomous aquatic vehicles today that use sonar, the AVs on the road are not utilizing sonar as it is only utilized while submerged under water.

In the previous blog post, the discussion was that current AVs are using technology like radar. Interestingly, Heinrich Hertz began developing the early radar system in the 1880s (Skolnik, 2018). Britain continued radar research for aircraft detection and by 1938, they had their first radar system called the “Chain Home.” Radar development was well underway, but by the beginning of World War II (WWII), Germany had progressed in the development of radar technology faster than any other country (Skolnik, 2018). It was used—at that time—as an all-day, all-weather, long-range sensor for target detection (Xu, Peng, Xia, & Farina, 2017).

Post WWII, the 90s saw an increase in radar information with the nature of the environment to be obtained from radar echoes— now in the 21^st century, the advances in digital technology have allowed the improvement in signal and data processing, with a developmental goal of having all-digital phased-array radars in the near future (Skolnik, 2018). Lidar, as mentioned in a previous post, means light detection and ranging. Simply put, it is the technique for determining the distance to any object through use of a laser beam and measuring how long it takes the light to return to the transmitter (Gregersen, 2016). The first lidar efforts were made in the 1930s, where the height of clouds were determinable through light pulses and when the laser was invented in the 60s, it progressed to accompany the 1980s invention of the Global Positioning System (GPS) as well as inertial measurement units (IMUs). IMUs made accurate lidar data possible (Gregersen, 2016).

The GPS we know today has humble beginnings as the Navstar Global Positioning System, a space-based radio navigation system owned by the United States Government. It has allowed for accurate positioning, navigation, and timing to the world since its first launch in 1978 (Department of Defense, United States of America & NAVSTAR, 2008). Now, this multi-use development has grown into a tool that is used globally; applications include, safety, economic growth, transportation safety, and it is now imperative to the global economic infrastructure (Department of Defense, United States of America & NAVSTAR, 2008). This is especially pertinent for AVs in order to pinpoint where they are at any given time.

From this brief discussion, the hope is that you—the reader—now understand where these technologies started, what their original applications were, and how they have progressed to being imperative to an AV’s operation. Without these nearly hundred-year-old inventions, the technology we know today as “AV” would not be able to navigate itself, avoid obstacles, or have any autonomous awareness of its surroundings—ultimately, this is the “smart” factor. Even from the humble beginnings of these inventions, they have grown into some of the most intelligent and imperative technologies to date, and without their early applications, we simply would not be where we are today.

References

Department of Defense, United States of America & NAVSTAR. (2008). Global positioning system standard positioning service performance standard [PDF file] (4th ed.). Retrieved from https://www.gps.gov/technical/ps/2008-SPS-performance-standard.pdf

Encyclopedia Britannica (2019, May 16). Sonar. Retrieved from https://www.britannica.com/technology/sonar

Gregersen, E. (2016, October 13). Lidar. Retrieved from https://www.britannica.com/technology/lidar

Xu, J., Peng, Y.N., & Xia, X.G. & Farina, A. (2017). Focus-before-detection radar signal processing: Part i—challenges and methods. IEEE Aerospace and Electronic Systems Magazine, 32(9), 48-59. doi: 10.1109/MAES.2017.160142

Skolnik, M, I. (2018, December 28). Radar. Retrieved from https://www.britannica.com/technology/radar/History-of-radar

Written by: Natasha Kowalskyj, Social Media Summer Work Study
Edited by: Isabella Blandisi-Van Hee, Project Coordinator for Applied Research

The future of cities being smart, artificially intelligent, and ripe with autonomous technologies is not as far-fetched or “science fiction” as previously thought. The Government of Ontario has created the Autonomous Vehicle Innovation Network (AVIN) and has partnered with Durham College (DC) to make DC one of the six Regional Technology Development Sites (RTDS). These sites span across Ontario and revolve around the development of new technologies, all with different areas of focus; DC narrowing-in on the Human Machine Interface and the User Experience (“Regional Technology Development Sites,” 2019). In particular, DC, with the help of AVIN, will have the opportunity and ability to secure, test, and promote autonomous vehicles for the benefit of Durham Region. AVIN’s five main goals are: 1.) to commercialize autonomous vehicle transportation and infrastructure; 2.) to encourage innovation and collaboration; 3.) to leverage Ontario talent; 4.)  to build awareness around the fact that Ontario is a world-leading automotive manufacturer, and finally; 5.) to support regional auto-brain belt clusters (Autonomous Vehicle Innovation Network (AVIN), 2019). There are a multitude of initiatives, benefits, and opportunities for communities to adopt emerging technologies within this industry, however before we delve into those aspects, this post will first cover the ways in which autonomous driver-less/driver-assist vehicles operate and what criteria is currently legal in Canada. In doing so, the reader will gain a basic comprehension on the various modes of vehicle “autonomy.”

From a ground-level understanding, there are different ways to describe the levels of artificial intelligence within autonomous vehicles (AV). Vehicles today already have ‘driver-assist’ technology with low-levels of automation like cruise control, lane assistance, and emergency braking. However, it is important to note that these only assist the driver—the human driver is still fully responsible to stay engaged in driving at all times (Transport Canada, 2019a). Basically, when the word “automated” is used, the vehicle itself does not have the ability, independence, or intelligence to carry-out necessary functions without a driver present. Autonomous, on the other hand, means there is full vehicle control or “autonomy” present, and when applied, it means the vehicle [not the human driver] controls all functions of operating. Therefore, when the term “autonomous” is used, the car is highly intelligent and independent—it could go as far as to set the route, drive to get there, and maintain all functions of driving without any human intervention (e.g., staying within lanes, maintaining a legal speed and parking). In sum, as the level of automation increases, the intervention of a human driver becomes less imperative and the vehicle becomes more autonomous (SAE International, 2017).

The Society of Automotive Engineers (SAE’s) have developed a comprehensive scale, which dictates the levels of automation and intelligence that a vehicle has in place. Ranging from 0 to 5, the available autonomy will now be discussed in-depth.  First, Level 0 (no automation) can be described as full-time control of the vehicle by a human driver, (SAE International, 2017) where there is no intervening system within the vehicle to assist the driver, (Skeete, 2018) and is essentially described as any car that relies solely on humans to perform tasks of driving, but may feature cruise control or crash warnings without intervention (Hyatt, 2018).

Next, Level 1 (drive assistance) means there is a driver-assistance system in place for the automation of either the steering or acceleration/deceleration (SAE International, 2017). In this level, the human driver is still necessary to perform all other tasks of driving like controlling the speed and driving safely (and everything in between). However, within this level, the driver could have assistance from the vehicle for parking (Skeete, 2018), lane-keeping technology, or adaptive cruise control (Hyatt, 2018). Essentially, a Level 1 vehicle will have one distinct advanced driver-assistance feature, the mobility is still supervised by a human driver but for convenience, the Level 1 AV can maintain its own speed (Hyatt, 2018).

Level 2 (partial automation) is not far removed from Level 1, however, Level 2 means the AV will have the ability to execute one or more driver-assistance systems, including steering and acceleration/deceleration (SAE International, 2017). With this said, the expectation is that the human driver will intervene when prompted by the vehicles to complete remaining aspects of driving within the autonomous vehicle, which would include everything aside from braking, steering, or acceleration— aspects of driving which a Level 2 vehicle can control with its multiple Advanced Driver Assistance Systems (ADAS) (Hyatt, 2018). An example of a Level 2 autonomous vehicle is the General Motors Super Cruise, Mercedes-Benz Distronic Plus, Nissan’s ProPilot Assist, and Tesla’s Autopilot vehicle are all vehicles with two or more assist technologies, making them Level 2 automated (Hyatt, 2018).

Level 3 (conditional automation), and beyond, is where the vehicles autonomy is increased drastically through its automated driving system, which monitors the driving environment; the AV can complete all aspects of driving with the expectation that the human driver will take over when prompted to intervene (SAE International, 2017). Any Level 3 AV is capable of taking full control of driving and operating during select parts when certain operating conditions are met, and the 2019 Audi A8 is aiming to be the first Level 3-capable vehicle brought to the public. However, they are still awaiting legal approval from many countries (Hyatt, 2018). From Level 3 and onwards, the AV is able to control operational features like steering, braking, and monitoring the roadway, other vehicles, as well as tactical tasks like changing lanes and using signals (SAE International, 2017). As of January 2019, according to Transportation Minister, Jeff Yurek, Level 3 conditional AVs are legal to drive on the roads in Ontario. This occurred after the province allowed the on-road testing of autonomous vehicles, which stems from a 10-year pilot project launched back in 2016 (The Canadian Press, 2019).

Next, Level 4 (high automation) means that little to no human interaction within the vehicle. At this level, even if the AV sends a request for the human to intervene and the human does not respond immediately, it can still perform all functions of driving autonomously (SAE International, 2017). While there are currently no Level 4 AVs on the road today, they are projected to be able to complete an entire journey without human intervention, while still featuring a steering wheel and pedals for when it is required that a human assume control of the vehicle (Hyatt, 2018).

Last, Level 5 (full automation) is the final level of vehicle autonomy presently on the market. Here, human driving is completely unnecessary, to the point where these vehicles are being projected to not feature any steering wheel or pedals (Hyatt, 2018). Defined as a fully autonomous end-to-end journey (Skeete, 2018), with the advanced software of vehicle-to-vehicle and vehicle-to-environment communications present, Level 5 AVs are set to be geographically unconstrained and will allow for all sorts of freedoms while commuting. They will even eliminate the need to own a car, in an AV future (Hyatt, 2018).

While these ADAS are being created, tested, and implemented into smart cities all around us, this does not mean we will be sitting in a car without a steering wheel any time soon in Ontario. The testing of such autonomous vehicle innovations will be ongoing for the foreseeable future (until perfected) in order to eliminate any of the potential risks, threats, or adversities like Canadian winters and how those uncontrollable factors will affect autonomy in vehicles. Until then, this blog will strive to keep you up-to-date on all of the latest innovations in this field.

References

Autonomous Vehicle Innovation Network (AVIN). (2019). Retrieved from https://www.avinhub.ca/

Hyatt, K., & Paukert, C. (2018, March 29). Self-driving cars: A level-by-level explainer of autonomous vehicles. Retrieved from https://www.cnet.com/roadshow/news/self-driving-car-guide-autonomous-explanation/

Mercer, C., & Macaulay, T. (2019, March 12). Companies Working On Driverless Cars You Should Know About. Retrieved from https://www.techworld.com/picture-gallery/data/-companies-working-on-driverless-cars-3641537/

Regional Technology Development Sites. (2019). Retrieved from https://www.avinhub.ca/regional- %20technology-development-sites/

SAE International. (2017). Automated driving levels of driving automation are defined in new SAE international standard J3016 [PDF file]. Retrieved from https://web.archive.org/web/20170903105244/https://www.sae.org/misc/pdfs/automated_driving

Skeete, J. (2018). Level 5 autonomy: The new face of disruption in road transport. Technological Forecasting and Social Change, 134, 22-34. doi:10.1016/j.techfore.2018.05.003

Transport Canada. (2019a, February 15). Automated and connected vehicles 101. Retrieved from https://www.tc.gc.ca/en/services/road/innovative-technologies/automated-connected-vehicles/av-cv-101.html#_What_is_an