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.

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.

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.

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. 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 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. 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.