Implementations occurring within Ontario

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.


980 CFPL Staff (2017, November 8). Premier Wynne set to announce funding for self-driving car research in Stratford. Retrieved from

Autonomous Vehicle Innovation Network (AVIN). (2019). Retrieved from

The Canadian Press (2019, January 23). Transportation minister announces driverless cars allowed on Ontario roads. Retrieved from

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 

SmartCone Technologies, Inc. (2019a, May 16). SmartCone Technologies’ “SAFETY FIRST” autonomous shuttle solution coming to Whitby, Ontario. Retrieved from