Report setup group2 2016

Introduction

In the introduction will be stated: the purpose of our assignment and the structure of the report.

Focus, Objectives and Approach

Focus

Our main focus area concerns: Automated traffic regulation (ATR).

Narrow that down is our specific focus: Updating the Current Intersection System to be Compatible with Autonomous Vehicles (Vehicle Intersection Control).

Restrictions on the focus area:

• Crossing has 4 directions.
• Traffic is randomly generated by a Gaussian distribution, the ratio between autonomous and normal cars will be changeable.

A multitude of traffic accidents happen at intersections^{citation needed}. These are also the bottlenecks in terms of efficiency since human drivers have varying reaction times. Drivers can also get stressed behind the wheel and lose valuable time while commuting. Contemporary intersections could make traffic more efficient by utilizing data from sensors of autonomous cars and controlling autonomous cars passing the intersection. By making the intersections smarter, user comfort can be greatly increased. Also society will benefit from more efficient driving past intersections, since emissions can be greatly reduced, which benefits the environment. Enterprises will also be positively influenced, since people will be able to get to work quicker instead of being stuck at an intersection.

Objectives

Main objective:

• Optimizing traffic flow at intersections by making them compatible with autonomous vehicles.

Objectives:

• Using sensor data from autonomous cars to make traffic light algorithms more aware of current traffic
• Choose a suitable communication protocol between autonomous cars and the intersection.
• Find existing efficient algorithms for autonomous cars at intersection in a literary review.
• Combine said algorithm with current traffic light algorithms to optimize traffic flow of both normal and autonomous cars.
• Make sure the traffic flow is optimal, which results in less waiting time and less emission.
• Create a transition solution that can combine the use of autonomous cars with human drivers by using the current intersection system.
• Keep in mind the perception of safety and the actual safety of passengers inside the autonomous cars (level of comfort).
• Decrease the number of traffic accidents involving cars on crossings.

Approach

The approach that is chosen is research and simulation oriented. Most information on existing solutions must come from literature and ongoing research. By identifying the state of the art, we will try to combine traffic light algorithms with algorithms that only work with 100% autonomous cars at the intersection. When such combination has been made, a simulation will be created and tested, after which an evaluation will follow.

Initial USE-aspects

Regarding the Focus, Objectives and Approach, USE-aspects need to be evaluated to see what important goals can be defined with regard to the algorithm and simulation. In this subsection, the most important aspects will be discussed.

Literature study

In this chapter the state of the art will be identified and summarized. Very specifically mention the research gap that was identified

Algorithm and Simulation

Algorithm

Here the choices of the algorithm will be stated. Like the requirements, preferences and constraints and how this was built toward the final algorithm and design of the intersection.

Simulation

Here the implementation of the algorithm is stated and the choices and methods for the simulation are explained. Like the choice to generate the traffic by a Poisson distribution and what the things are that we designed but did not include in the simulation.

Communication protocol

In order to make the algorithm work, a communication protocol has to be implemented in the intersection system. Following the literature research on current communication systems that exist between intersections and busses and ambulances, the KAR system was identified. This is a system that is already present in current intersections. It is based on a short distance radio.

When implementing it into the situation with autonomous cars, every autonomous car will be able to send information on their position, provided by their navigation system, to the intersection. The intersection is then able to know where every autonomous car is on the intersection. In reverse, the intersection is able to send information to the autonomous cars as well. In that way, the cars can anticipate on the time they will have to wait at the intersection and adjust their speed accordingly so a minimum amount of breaking and quick speed reduction is required, which increases the comfort for the autonomous car users.

It is also possible to implement a reservation system into the KAR system as it is already used for buses. The bus can now send their route information to the intersection so that the intersection knows how high the priority for that bus is. The same system could be used for autonomous cars. However, the question is how fair this is toward human drivers who will not be able to send their level of priority to the intersection. Therefore, it might not be user-friendly to implement this part of the KAR system into the intersections.

USE aspects

In this chapter the USE-aspects of the algorithm will be explained and evaluated. The traffic in the simulation can be divided into two categories: the cars and the pedestrians. Both of these categories are influenced by the behaviour of the intersection system. Therefore, both of these have to be evaluated. In the first paragraph, the USE-aspects for the cars will be discussed where a distinction is made between autonomous cars and non-autonomous vehicles. In the second paragraph, the USE-aspects for the pedestrians are discussed.

USE-aspects for cars

• Cost function

The algorithm is based on a cost function, which allows for inclusion of different influences that affect the users. As stated in the Algorithm section, the cost-function for the cars is:

[math]\displaystyle{ Cost_{lane} = (N + R) + C_1 \times t + P \times (t\gt T) }[/math]

The different variables are explained in the Algorithm Chapter. This equation indicates that the cost for the traffic consists of the number of registered autonomous cars and the number of non-autonomous cars that have been registered by the autonomous cars and the detection loops that are present in the intersection. The cost of the wait time is determined by the time that the cars are waiting at the intersection and an arbitrary constant which defines the importance of the waiting time. The last term is the cost of the wait limit. The maximum waiting time is defined by T. Whenever the actual waiting time exceeds the maximum waiting time, the cost for this lane will increase significantly as P is a constant that is arbitrarily large.

Another noticeable aspect of the cost function for cars is the term N+R, where N is the number of autonomous cars that the intersection has registered and R is the number of reported non-autonomous cars that have been detected by the autonomous cars. This means that the cars that are not at a detection loop or are directly behind or in front of an autonomous car, will not be registered in a lane. This means that in case a lane consists of several autonomous cars and a few non-autonomous cars, this lane will be prioritized over a lane that may consist of a larger total number of non-autonomous cars as sketched in the image below.

In the above situation the red dots indicate non-autonomous cars and the blue dots indicate autonomous cars. In the lane with five non-autonomous cars only the first car is registered by the detection loop in the intersection. In the lane with four cars, the two cars that are autonomous register two non-autonomous cars. Should the cost function be built in a way that this problem frequently occurs, this would be unethical. However, there are other factors that even this problem out. In reality, the situation sketched above is extreme as also other factors play a part in determining the priority of the lanes. Autonomous cars will generally be spread over several lanes, especially when the percentage of autonomous cars on the road increases. This indicates that the difference in situations between the lanes will be less than what is sketched in the above situation.

From this analysis can be concluded that in extreme situations (especially when there are still only a few autonomous cars on the road), the cost-function can behave unethically. However, in reality the situation is more nuanced by influences from several lanes and pedestrians.

• Green-wave aspect

The idea of the “green wave” for autonomous scars is that the intersection sends information about the current states and expected states to autonomous cars that are nearing the intersection. This information has a certain error that decreases as the car comes closer to the intersection. As a consequence, the autonomous car will adjust its driving speed to the information that it receives. This corresponds with the known principle of the “green wave” where drivers are notified via a sign that, if they drive a certain speed that is either equal to or lower than the maximum speed, they will have green light for an indicated number of times. Not every driver tends to stick to the advised speed that is indicated on the “green wave”-sign. Therefore, irritations and aggressive behaviour can be provoked when the fact that autonomous cars adjust their speed to the information provided by the intersection. In order to maintain the safety of the users of autonomous and human-driven cars, the human drivers need to be provided with information on the behaviour of the autonomous cars while they are driving so that they know what to expect when approaching an intersection.

In order to increase acceptance, it is important that drivers know what they can expect when nearing a crossing. Without any knowledge about how autonomous cars work, drivers might become annoyed by autonomous cars that adapt their speed to below the speed limit. A way in which acceptance of for example waiting time is increased among cyclists and pedestrians and that also decreases the aggression and risk of them running a red light is by implementing a traffic light that shows how long it takes before their light turns to green. The advantage here is that people know what to expect when waiting at a traffic light. While nearing a crossing without information that autonomous cars are adapting their driving, human drivers may become confused.

As the previous shows that drivers need to know what to expect, signs can be put up beside the road that indicate that autonomous cars are adapting their driving to the situation handed by them through the intersection system. It will also help to send the human drivers approximately the same information as the autonomous cars, which might stop human drivers from feeling deprived of advantageous information. They will also know exactly what behaviour to expect on the road as they approach the crossing, which should stop some of the aggression and might make some people follow the lead of the autonomous cars. If the information that the autonomous cars genuinely leads to greater efficiency, it is expected that some of the human drivers will realize this and start following the autonomous cars. This could then indirectly lead to platooning with human drivers and autonomous cars if the autonomous are clearly recognizable for the human drivers.

In order to implement the signs for the human-drivers, the currently existing green wave LED-signs may be used. Additionally, a larger sign may be placed along the road that shows an image of the autonomous car situation.

• Safety

Planning and task division

This is included as an appendix to the report.