Report setup group2 2016

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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 intersectionscitation 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.

Keeping in mind the initial estimated market share of autonomous cars

This is a USE-aspect from a society point of view. The crossing that is to be design should be compatible with the transition period after the introduction of autonomous cars on the market. In the beginning, the market-share of the autonomous cars will be relatively low compared to cars with human drivers. However, when the autonomous vehicles have been on the market for some time, the expectation is that the market-share will increase as society becomes more used to the presence of autonomous cars on the road. This means that the intersection should be usable for a minority as well as a majority of autonomous cars.[1]

As the percentage of autonomous cars on the crossing increase, this will impact the traffic situation. Human drivers might become a minority if the launch of autonomous cars is a success. This gives way for more advanced behavior from the autonomous vehicles. An example of this could be that when there are multiple autonomous cars subsequently nearing the crossing, they could use platooning to cross the intersection more efficiently. However, this is a hypothetical theory about the efficiency of autonomous cars that might be outside the scope of this research.

In the simulation the percentage of autonomous cars will be changeable so the results of the algorithm can be viewed for different market shares.

Viewing autonomous cars as lounge cars that adjust driving behavior to perfect timing and comfort

In the future, a plan for the autonomous cars is that they function as lounge cars for people to order to come and pick them up with the assignment to take them to the right destination at a certain arrival time. This image is envisioned by for example the CEO of Tesla, Elon Musk and the CEO of the taxi service Uber Travis Kalanick.[2]

However, it might be a more distant future image than is aimed for with this research. As previously mentioned, the full potential of this service might be reachable when the market-share of autonomous vehicles is increasing. Realizing a perfect solution for this aspect of autonomous driving when utilizing existing technology might therefore be outside the scope of this research.

Safety systems in communication between autonomous cars and the intersection

One of the keys to the customer acceptance of autonomous vehicles is decreasing the risk of hacking. Without means to stop this, customer acceptance will never be optimal.[2] However, the way to stop this lies in complicated software which would be a project alone to construct. It is possible however, to keep in mind the possibility of communication failure between the car and the crossing.

The risk of hacking however, is present in many modern technological applications. Personal computers are very sensitive to hacking and yet the majority of society has accepted the risk. A more recent example is the introduction of drones that are used in the airforce or modern surveillance technology. Although these technologies lead to many controversial debates, there is a large number of people who endorse the use of these systems.

In[3] it is argued that modern society itself is a “risk culture”. Society is constantly searching for new energy sources and technological advancement that can drive the earth forward. People are getting used to putting their trust in new systems since new technologies are increasingly being introduced into their lives. Risk nowadays also varies between social groups and cultures. This indicates that some groups will be more risk-taking than others and also that autonomous vehicles have a good chance of being accepted by the group that is most risk-taking and then gradually accepted by other cultures that are more apprehensive, despite the fact that the technology carries with it the risk of hacking.[3] This paints a more nuanced picture of the implementation of new technology into society, which many business and market studies do not consider.

Maximising the throughput while keeping in mind the safety of the users

When human drivers are passing the intersection, it will not feel safe to them if autonomous cars start planning ideal trajectories around them and overtake them on the right side of the road or cross right in front of their bumper because the autonomous car calculated that this would be possible. This is very risky since human drivers are easily scared and can react unpredictably and cause unavoidable collisions. When this occurs, another problem arises: how to collide as ethically correct as possible… Even if it might not be as efficient, autonomous cars will need to keep a certain distance to human drivers in mind.

However, one of the main advantages of autonomous driving is the predicted increased efficiency as well as safety. This indicates that a tradeoff has to be made between the efficiency of the autonomous cars and the safety and the perceived safety of the human drivers, in other words an optimization problem.

In the case of the perceived safety of human drivers, this is influenced by the way in which the autonomous cars plan their trajectory. If they plan their trajectories more aggressively (for example overtaking on the right), this will increase the discomfort of the human drivers. This aggression can be simulated by increasing or decreasing the constraints bound to the freedom of trajectory planning for the autonomous cars. What is less obvious is how to measure the level of discomfort of the human drivers, without the pitfall of resorting to simplified if-then reactions.

Efficiency or throughput however, is more easy to measure. For this, the mean time cars spend on a normal, autonomous car free crossing can be used. If after implementation of the new crossing this mean time decreases, the efficiency has increased.

The environmental factor

Society is involved in heavy environmental debate in which traffic plays an important role. The implementation of autonomous cars and taxi services, should decrease the CO2 -emission by their increased traffic efficiency. This means that if the throughput of the intersections is increased, society will benefit in environmental terms.

By using the mean carbon footprint of cars, the emission on the crossing can be measured. This will be an additional result to the optimization problem introduced above.

Conclusion

The market-share of autonomous cars will change rapidly in the transition period, which is the setting of this research. Therefore, it is important to consider the influence of this change when designing the intersection.

Focus is kept on the begin phase of the introduction of autonomous cars, which means that considering fully operational autonomous taxi services is outside the scope of this research.

The implementation of safety systems is an important factor in the introduction of autonomous vehicles. Even though the design of such software is subject matter for future work, in this design problem communication failure and the consequences of this can be considered.

An optimization problem is introduced when considering the (perceived) safety of human drivers and the efficiency of the intersection. The latter can be measured and identified, while the human factor of perceived safety leads to reactions that are more complicated to consider, without resorting to unrealistic simplifications.

Conclusion

Literature study

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

Algorithms and plans for intersections with autonomous vehicles

Intersection algorithms

Sensors in automated vehicles

Current communication systems

As one of the objectives is to incorporate current technology into the design of the intersection that is compatible with autonomous vehicles, research has to be done to determine what communication systems are currently embedded in intersection systems.

VETAG/VECOM

These are systems that can be found in current intersections. VETAG stands for vehicle tagging and uses an induction-loop that is built into the crossing. This loop receives information from buses and ambulances through transponders that are built into the vehicle. [4]

The induction-loop sends out a signal every few time steps and if there is a vehicle with a transponder located above the induction-loop then the intersection will receive information on for example the bus number and bus line in order to know where the bus is going. The correct traffic lights can then become green. [4] [5]

Vecom stands for vehicle communication and is an extension of the Vetag technology. With Vecom, the induction-loop is able not only to receive information, but it can also give information to the vehicle itself; this can be a signal that indicates what crossing the vehicle is nearing. [4]

KAR

The KAR system stands for korte afstands radio in Dutch, which means short distance radio. This system is based on GPS signals, which permanently keeps track of the location of the vehicle by using location determination systems in the vehicle itself. This system eliminates the use of induction-loops, which is beneficial since these can be quite costly.[5][6]

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. [7]

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. [7]

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. The system should be used though when an ambulance or police car approaches. [7]

Another aspect of the communication protocol is to make use of the detection loops that are embedded in the roads. These differ from the more expensive communication loops that are used by the VETAG and VECOM systems as described in the literature study, in the sense that they only register when there is a car on top of the loop. This system can register only that there is a car and not how many cars are present. So whenever there is a non-autonomous car present at the intersection on the loop and there are no autonomous cars that can register this car, the vehicle will still be registered.

By using the communication protocol described above, the currently embedded software can be utilized without having to rebuild the entire intersection and changing its systems.

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.

Example of priority issue in the cost function

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

As mentioned in the Focus, Objectives and Approach chapter, the user might be fearful of malfunctioning of the technology as this could be life-threatening. To this end, the simulation includes the case in which packet loss occurs. When the communication with the intersection fails, the autonomous vehicles will not receive any information on the states of the intersection. This will imply that the autonomous car starts behaving like a human-driven vehicle and react to the situation as it is. This will not endanger any of the passengers in the autonomous car where the packet loss occurs or the surrounding traffic. However, it will take away the advantage of receiving the information from the intersection and therefore the amount of comfort the passengers experience will decrease.

USE-aspects for pedestrians

The cost function for the pedestrians is quite similar, with terms for the wait time and the wait limit as shown in the following equation.

[math]\displaystyle{ Cost_{pedestrian lane} = C_2 \times t + P \times (t\gt T) }[/math]

Noticeable is the exclusion of the N+R term from the pedestrian cost function. This is because the number of pedestrians is not measurable when using the current technology in the intersection. This would indicate that the cost for the pedestrians is generally lower than the cost for the cars, which would prioritize the cars over the pedestrians. This is not user-friendly as pedestrians are often exposed to weather while waiting. If they are always forced to wait the maximum waiting time, this would create a large level of dissatisfaction with the pedestrians and this would discourage people to come by foot, which would indirectly be bad for the environment. The way in which this is solved is by tuning the [math]\displaystyle{ C_1 }[/math] and [math]\displaystyle{ C_2 }[/math] constants so that these bring the cost functions closer together. Also in the case of bad weather, the constant [math]\displaystyle{ C_2 }[/math] may be tuned to have even more impact to ensure the comfort of the pedestrians. Also, in case several intersection states have the same cost, the priority will go to the states that allows for the pedestrians to cross that have waited the longest.

Since waiting longer than maximally tolerable is not user-friendly, the value P is arbitrarily large to ensure that no lane waits more than the maximum allowed time T. This is applied for pedestrians as well as cars.

When the cost of several states are equal, pedestrians are prioritized because of their generally lower level of comfort (due to being exposed to weather and other factors outside at a busy intersection). Also their cost function is brought closer to the cost function of the cars by tuning the [math]\displaystyle{ C_1 }[/math] and [math]\displaystyle{ C_2 }[/math] constants, which results in less prioritizing of cars over pedestrians due to the missing traffic factor in the pedestrian cost function.

The cost function as presented will ensure the dynamic behaviour of the intersection, which makes sure to increase efficiency to benefit user, society and enterprise, but which also increases the fairness of an intersection algorithm as for example more vulnerable users like pedestrians are prioritized when the cost of several states is equal.

Results

Evaluation

Validation

References

  1. ↑ Unknown. (2015, 08 18). Forecasts. Retrieved from Driverless-future: http://www.driverless-future.com/?page_id=384
  2. ↑ 2.0 2.1 Intelligence, M. (2016). Autonomous/Driverless Cars – Market Potential Estimation and Possible Competitive Landscape – Forecasts, Trends and Analysis (2016 - 2021). Retrieved from Mordorintelligence: http://www.mordorintelligence.com/industry-reports/autonomous-driverless-cars-market-potential-estimation?gclid=CJmT0Me6oM8CFYu6GwodhhABbw
  3. ↑ 3.0 3.1 Flynn R., Bellaby P. (2007). Risk and the Public Acceptance of New Technologies. New York: Pelgrave Macmillan.
  4. ↑ 4.0 4.1 4.2 Techniek, S. (sd). VETAG/VECOM/SICS. Opgehaald van ssstechniek: http://www.ssstechniek.nl/?page_id=40
  5. ↑ 5.0 5.1 Crow, K. (2015). Selectieve detectoren. Opgehaald van Crow: http://www.crow.nl/vakgebieden/verkeer-en-vervoer/verkeersmanagement/verkeersregelinstallaties/regelingen-voor-specifieke-doelgroepen/verkeerslichten-en-hulpdiensten/selectieve-detectoren
  6. ↑ Gelderland-Midden, V. e. (sd). Ambulances sneller door KAR. Opgehaald van vggm: http://www.vggm.nl/ambulancezorg/over_de_ambulancezorg/nieuws_ambulancezorg/nieuwsarchief_ambulance/ambulances_sneller_door_kar
  7. ↑ 7.0 7.1 7.2 Duwel, P. (2008). KAR'en maar! Korte Afstand Radio voor prioriteit bij verkeerslichten. Rotterdam: Kennisplatform Verkeer en Vervoer.

Planning and task division

This is included as an appendix to the report.

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