Fully+Autonomous+Vehicle+Integration

=**Introduction **=

toc A driver-less car is an automobile that has an autopilot system allowing it to safely move from one place to another without help from a human driver. Currently, as of July 2017, entirely autonomous vehicles are not permitted on public roads. Autonomous features may be present in cars, but full autonomy does not exist. Full autonomy would mean that every car on the road communicated with each other constantly and all decisions are processed through a computer. Research is showing there would be no need for traffic lights at intersections, traffic would be mitigated, car related accidents would be almost entirely prevented, and fuel efficiency would lower greenhouse emissions.



=Fuel Efficiency =

Intersections
One of the main reasons for ‘stop and go’ traffic is red lights. Traffic lights control the flow of traffic in heavy populated intersections. While one side of the road is driving, the perpendicular road, filled with cars, must wait until the traffic light allows the cars to go. Traffic lights exist to prevent accidents from happening in an intersection. Humans do not have the capability to maneuver through high congestion and multiple directional traffic. Computers, however, have the ability to process faster than humans and can be more precise. Put them together, and we may see the end of the traffic light. The semi-autonomous driving aids being fitted to many new cars could consign the red light to history [1]. The less time cars spend at intersections, the less fuel they will consume. A gasoline car, when idling, looses 3% of fuel [2]. An algorithm developed by a team of researches shows just how much traffic can be reduced. Simulations of the algorithm show that the minimizing total delay algorithm can also minimize the total number of stops, and hence, is more suitable for implementation [3]. media type="youtube" key="4pbAI40dK0A" width="560" height="315" ===**Traffic Jams** === Similar to traffic intersections, traffic jams may be mitigated with the implementation of autonomous vehicles. In 1999, Konishi et al. (1999) proposed a CM (closed map) model and suggested a delayed-feedback control method to suppress the traffic jam. They investigated the noise effects under open boundary bottleneck conditions via computer simulations, showing that the traffic system could run well under proper control scheme [4]. Many types of models were proposed and studied to prevent congestion. The study done by Hong-di He in China showed that their model could not only suppress traffic jams, but also reduce energy consumption. Communication between cars could greatly increase efficiency of time and fuel. Again, though, this would require all cars to be able to communicate with each other. If autonomous and non-autonomous cars drove on the same road, miscommunication could lead to deadly accidents. Collision warning applications have been proven to have an impact on drivers' behavior: benefits such as the improvement of the reaction times in incidents and the reduction of accidents rate have been shown [5].

Efficiency is the most important factor in controlling the amount of fuel consumption.  only about 14%–30% of the energy from the fuel put in a conventional vehicle is used to move it down the road [2].  The less time a car is turned on, the less fuel it uses. Efficiently transporting people from destination to destination affects the time spent in a car. This is mitigated by preventing traffic jams and preventing the stopping of cars at intersections. Engine technology has evolved over the last decade to use less fuel when driving. About 74%–94% of the energy used to power an electric car is used to move it down the road, depending on the drive cycle. Electric cars are more efficient than comparable conventional vehicles, especially in stop-and-go driving, due to the use of regenerative braking and start/stop technologies [2]. With these factors combined, transportation vehicles could become much better and more efficient at transporting people.

Taxi and Delivery Services
Services such as Uber and Lyft have increased in popularity in the last couple of years. The idea of using your own car for transporting other people has become immensely convenient for college students and weekend festivities. The use of taxi services is dwindling. Taxi use is down 63% since the first quarter of 2014, according to Certify’s quarterly SpendSmart report [6]. Autonomous vehicle transportation services may be the next big thing. Fully automated taxi services could result in decreased US per-mile greenhouse gas (GHG) emissions in 2030 per AT (autonomous taxis) deployed of 87–94% below current conventionally driven vehicles (CDVs), and 63–82% below projected 2030 hybrid vehicles, without including other energy-saving benefits of AVs (autonomous vehicles). With these substantial GHG savings, ATs could enable GHG reductions even if total vehicle miles traveled (VMT), average speed and vehicle size increased substantially. Oil consumption would also be reduced by nearly 100%. [7]. Because autonomous vehicles are mostly developed on the electric vehicle platform, it will help to provide a clean and greener environment. In addition, the idea of ride-sharing using the autonomous vehicles will reduce the entire vehicles population as well as the parking areas in the cities [8] [9].

The U.S delivery service is bigger than it has ever been. People shop online and have items shipped to their front door. Cars and trucks drive across the country to deliver all sorts of items. Drivers of such vehicles steer for long periods of time, and can get exhausted. This is only one of many examples that show the benefits of autonomous vehicles. Shipping delivery times could be reduced while saving fuel. As driver-less cars merge into our transportation system in the coming years, some researchers believe autonomous vehicles may save fuel by trailing each other in large platoons [10].

=**Saving Lives** =

Traffic Fatalities
In 2015, 35,092 lives were lost due to vehicle related accidents [11]. Autonomous cars aim to reduce this staggering number. Communication between cars could allow for quick responses and thus prevention of injury. Research teams have tried using algorithms and sensor technology to help prevent collisions. A group of researchers at Carnegie Mellon College of Engineering examined forward collision warning, lane departure warning, and blind spot monitoring systems which makes up 24% of all crashes. The team of engineering researchers concluded that the public could derive economic and social benefits today if safety-oriented, partially automated vehicle technologies were deployed in all cars [12]. These simple warning systems have proved to be reliable, so the next step is for cars to automatically apply the maneuvers. Collectively, these technologies could prevent or reduce the severity of up 1.3 million crashes a year, including 10,100 fatal wrecks [12]. Some of the existing automated features include ADAS which consists of Adaptive Cruise Control. Automated parking has been a huge success in the market and has helped those who could not park in a tight space. Collision avoidance systems have become more reliable in newer models of cars. Lane departure warnings now have the ability to override the driver if no further actions are taken by the driver [8].

=<span style="font-family: Arial,sans-serif; font-size: 12pt;">**Conclusion** =



<span style="font-family: Arial,sans-serif; font-size: 12pt;">**The Knowns**
<span style="font-family: Arial,sans-serif; font-size: 12pt;">Automated features in cars today have already proven to be more reliable. Systems such as Lane Keeping Systems prevent drivers from veering out of a lane because of possible drowsiness. Cars are fit with automatic braking systems that activate before the driver can react, saving a life. Current research focuses on the full autonomous systems. Car to car communication will help prevent traffic jams, create smoother and safer intersection crossings, all the while lowering total fuel consumption. One of the major problems of road traffic globally is road congestion. In addition to the collision risks due to the heavy traffic congestion, according to the Bureau of Transportation Statistics of United States, the aforementioned problem is responsible for about a third of vehicle carbon emissions in the United States [8] [13].

<span style="font-family: Arial,sans-serif; font-size: 12pt;">The Unknowns
<span style="font-family: Arial,sans-serif; font-size: 12pt;">The coordination problem for cooperative, autonomous vehicles is like none other before. The ability to communicate between cars will be difficult to maintain due to the complex networks that already exist. Scalability and robustness are still challenging problems that deserve further study, in particular, with respect to inherently unreliable exchange of information and limited sensing of the surrounding environment [14]. <span style="font-family: Arial,sans-serif; font-size: 12pt;">We do not know how governmental officials will install laws to help autonomous cars become a reality. There are ethical questions that have not been answered. Self-driving cars can efficiently get people to places while consuming less fuel. However, car accidents will still occur and the responsibilities are still unclear for who to blame.

=<span style="font-family: Arial,sans-serif; font-size: 12pt;">References =

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[1] B. Yang and C. Monterola, "Efficient intersection control for minimally guided vehicles: A self-organised and decentralised approach", //Transportation Research Part C: Emerging Technologies//, vol. 72, pp. 283-305, 2016.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[2] "Where the Energy Goes: Gasoline Vehicles", Fueleconomy.gov, 2017. [Online]. Available: https://www.fueleconomy.gov/feg/atv.shtml. [Accessed: 05- Jul- 2017].

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[3] S. Ilgin Guler, M. Menendez and L. Meier, "Using connected vehicle technology to improve the efficiency of intersections", Transportation Research Part C: Emerging Technologies, vol. 46, pp. 121-131, 2014.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[4] H. He, C. Zhang, W. Wang, Y. Hao and Y. Ding, "Feedback control scheme for traffic jam and energy consumption based on two-lane traffic flow model", Transportation Research Part D: Transport and Environment, 2015.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[5] A. Orozco, S. Céspedes, R. Michoud and G. Llano, "Design and simulation of a collision notification application with geocast routing for car-to-car communications", European Transport Research Review, vol. 7, no. 4, 2015.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[6] "Uber, Lyft overtake taxis for business travel expenses", USA TODAY, 2017. [Online]. Available: https://www.usatoday.com/story/travel/roadwarriorvoices/2016/11/06/uber-lyft-expenses-business-travelers/93240430/. [Accessed: 05- Jul- 2017].

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[7] J. Greenblatt and S. Saxena, "Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles", Nature Climate Change, vol. 5, no. 9, pp. 860-863, 2015.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[8] U. Hamid, K. Pushkin, H. Zamzuri and M. Rahman, "Current Collision Mitigation Technologies for Advanced Driver Assistance Systems – A Survey", PERINTIS eJournal, vol. 6, no. 2, pp. 78-90, 2016.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[9] Lam, A. Y., Leung, Y. W., & Chu, X. (2016). Autonomous-Vehicle Public Transportation System: Scheduling and Admission Control. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1210-1226.

<span style="color: #333333; font-family: Arial,sans-serif; font-size: 12pt;">[10] A. Adler, D. Miculescu and S. Karaman, Optimal Policies for Platooning and Ride Sharing in Autonomy-Enabled Transportation. Cambridge: Massachusetts Institute of Technology, 2016.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[11] "General statistics", Insurance Institute for Highway Safety, 2017. [Online]. Available: http://www.iihs.org/iihs/topics/t/general-statistics/fatalityfacts/state-by-state-overview. [Accessed: 05- Jul- 2017].

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[12] C. Harper, C. Hendrickson and C. Samaras, "Cost and benefit estimates of partially-automated vehicle collision avoidance technologies", Accident Analysis & Prevention, vol. 95, pp. 104-115, 2016.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[13] Duranton, G., & Turner, M. A. (2011). The fundamental law of road congestion: Evidence from US cities. The American Economic Review, 101(6), 2616-2652.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">[14] R. Hult, G. Campos, E. Steinmetz, L. Hammarstrand, P. Falcone and H. Wymeersch, "Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation", IEEE Signal Processing Magazine, vol. 33, no. 6, pp. 74-84, 2016.