Driverless, Non-autonomous Traffic Management System | Reflective Research Report

Autonomous Traffic System Advancements today

According to Techopedia (2019), an autonomous system is meant to be a collection of networks or a network itself which is being controlled and monitored by one entity or a center. There have been many imaginations of different autonomous systems in the human brains and one of the most discussed system is the autonomous traffic system. The reason for its popularity is the advancements that haven been made so far. Virginia Tech Transportation Institute (VTTI) have funded $7.5 million to collaborate with other transportation stakeholders to see how the current situation has been with respect to roads and accidents and how an autonomous system can be beneficial (Stone, 2020).

Likewise, this technology is being tested by many mainstream companies. Waymo, Google’s sister-company Alphabet’s self-driving unit, held successful automated taxi experiments in California, carrying more than 6,200 passengers in the first month and thousands afterwards. For autonomous vehicles they represent a realistic business case. Companies such as Walmart are using autonomous cargo for their deliveries. Meanwhile, Pizza hut is also collaborating with Toyota to make autonomous cooking plus delivery vehicle. After its launch, Tesla has delivered more than 780,000 vehicles to consumers, most of which come with pre-installed, self-driving capabilities available to users who buy the software needed. More companies like Ford, Nissan and Mercedes are all extensively working on this technology (Harris, 2020). Though this technology so far has had lots of success but making it into a close system is still difficult.

How to operate an autonomous traffic system?

In this world any technology that can be seen today and how it operates with just few or neglecting imperfections, it’s all a matter of slow progress made. Autonomous traffic system is also no exception and according to Shuttleworth (2017) and Bernard Marr (2019) there are total of 5 levels (1-5) for this system. Level 1 refers to manual vehicles, level 2 refers 2 driver is provided with some features but still most tasks done by the driver, level 3 refres to more features such as braking is introduced, level 4 refers to car is on self driving mode but still driver must be aware to take control if needed and level 5 is full automated.

This system does require level 5 autonomous vehicles and currently the level5 does not exist in the today’s context. It have been said that by 2020 the world will be able to see the prototypes of these vehicles (Bernard Marr, 2019). Yet things don’t go according to plan and hence till now no level 5 vehicles have been seen. Later on, this will be explained why level 5 autonomous vehicles only.

There are many other additional plannings required of this system and to mention few are roadway, transportation prices, parking, curb management, public transit needs and other implications such as demographic changes (Litman, 2020). Other than the planning itself the communication system as an autonomous system depends on the center which is regulating it. Such as according to Mraz (2019), the vehicles need to be programmed carefully in order for this tool Flow to use traffic control data from local smart cars and networks to effectively become mobile traffic managers and also they will use a cloud-based system.

After the automation of cars and system planning comes the law and legislation infrastructure for any state to operate a system on large scale. There has been an increase in the legislation related to autonomous since 2012. New Federal Guidelines for Automatic Driving Systems (ADS) have been issued by the National Highway and Transportation Safety Administration (NHTSA). A Roadmap for Safety 2.0, the latest guidelines to companies and states on autonomous driving systems and many more reaching the legislation upto 29 states (National Conference of State Legislatures, 2020). But there have been some downfalls too. Such as when Uber self-driving car killed a women, leading to a consideration that technology is being implemented before a proper legal system (Marshall, 2019).

Data Requirement

The first data that will be collected will be from the vehicles themselves. According to Dmitriev (2019), an autonomous car will generate more than 300 TB of data anually and even now the incomplete autonomous vehicles can produce almost 25 GB of data per hour. The reason for this data is that these vehicles have manyh sensors, and more sensors means more data. This data is required to calculate the speed, accelration and also to navigate the direction in which to travel. This data will help the center to control the speed, movement of vehicles, avoid obstacles and read approprate markings such signs on road (DXC Technology, 2019).

The second crucial data is of detailed road networks information. These road networks consist of highways, roundabouts, links, connection nodes and many more features that make up the road networks. This system needs an upto date and real time analysis of the current road map or else the whole system can collapse. These vehicles can provide the option for choosing the option to where to go by using road map and then the facility of GPS (Zein, Darwiche, & Mokhiamar, 2018).

There will be data needed for parking, charging and mantainance stations. As this system will be autonomous and likewise the private car owners have their cars available in no time, this will be demanded of the passengers too. Hence for a vehicle to reach to its client there must be a parking space available within a mile radius (Litman, 2020). It is important to keep track of all the available spots. The parking data is also required for the unloading and loading services for shops, if the parking is to be done on streets.

Other important data are the fares, if the transportation is provided free first its wrong as there must be some road charges and other to have control travelling to some extent. This will require data of passengers in the vehicle and distances each one of them have travelled (their pick-up and drop-off location) (Litman, 2020). Just like uber, this will be a service provided by this system and charging its cost and number of passengers detail will also avoid empty vehicle on the roads hence reducing congestion.

HD maps are also a pre-requisitive for this system, these maps will help these vehicles to know the simple things as speed breaks to a person walking on the street. These maps are important to avoid the accidents which is an initial aim through this system (Seif & Hu, 2016).

E-R Diagram

This E-R diagram gives a high-level representation of this system’s information system. The center will be the controlling point of this system hence the weather information will be directly access so accordingly they can manage the system. Center must also have information of parking as mentioned above why. Refueling station are connected with locations along with the information that the specific location have vehicle to make routing easier for these vehicles to nearest station. Vehicle and Passengers have a junction table storing the trip details. Car data would have data collected from the vehicle itself such as speed, acceleration and so on.

Benefits

This will benefit in social, economic and environmental way to our society. The social way that it will reduce the stress of the passenger by relaxing them by not worrying about their current environment. This will also increase the productivity of a person first by the time they drive they can do something productive or relaxing in their vehicle. This will increase the mobility for non-drivers such as elderly and in genera those who cannot drive as this system does not require a driver and hence increase the independence of all age groups. This will flourish the car sharing or carpooling which will reduce the need for owning a car and hence reduce amount of vehicles on the road (Litman, 2020). The most important social impact would be the reduce accidents. According to Brown (2017), 37461 people died in 2016 due to accidents and out of which 94-95% were due to human error to small errors such as not noticing a person crossing, or because of driving while being intoxicated. This autonomous system will eliminate these risks and have a huge impact on the annual deaths toll due to road accidents.

According to Intel Corporation and Strategic Analyst its been estimated that this will have an economic impact of $7 trillion in 2050. This will happen as there will lesser crashes so this will eliminate the cost of medical, insurances, reparing services and traffic police will decrease too as the system will be controlled by the center. There will be reduced traffic congestions as its been estimated that 25% of the road congestions are due to accidents. Due to less congestions and traffic, people will have lesser travelling on average. Its estimated that in US people spend $2.7 billion hours (as they earn hourly) by driving their way to work (Tomita, 2017). As these vehicles will be driven efficiently, this means lesser fuel will be consumed boosting our eco-system drastically.

As discussed the reduced fuel consumption due to efficient routes and lesser human errors such as sudden acceleration means lesser emmission of greenhouse gassess into our atmosphere and hence this would reduce the impact of the global warming drastically without disrupting the economy (Chang, 2018). This will transform our landscape of urban areas as the roads will be used efficiently there will be lesser need of vehicles hence increase road capacity and reduced parking cost (Litman, 2020).

Limitations

The first limitation is the cost itself of designing this vehicle and may increase cost by additional services or equipment. There will also be need for maybe a better or little more advanced infrastructure which would again increase expenditure. This may also reduce other mobility options such as cycling, walking, etc. As the vehicles data will be recorded all the time this will increase the vulnerability of a data breach and hence would require a stronger security system (Litman, 2020).

This is important to note that the lines that mark lanes frequently vanish under extreme weather situations where it snows especially heavily under winter. Cameras are used to power self-driving cars and in blizzards, blinding winds, fog or other conditions where visibility is poor, these systems are made virtually useless. However, this has proved to be such an issue for the technology companies seeking to develop self-driving cars that Google and several other organizations have reported to regulatory authorities in many states that human drivers were expected to take care of their autonomous self-driving vehicles during these exact environmental conditions.