Reducing Side-sweep Accidents with Vehicle-to-Vehicle Communications 2017

This dissertation present contributions to the understanding of the causes of a side-sweep accidents on multi-lane highways using computer simulation. Side-sweep accidents are one of the major causes of loss of life and property damage on highways. This type of accident is caused by a driver initiating a lane change while another vehicle is blocking the road in the target lane. Our objective in the research described in this dissertation was to understand and simulate the different factors which affect the likelihood of side sweep accidents. For instance, we know that blind spots, parts of the road that are not visible to the driver directly or through the rear-view mirrors are often a contributing factor. Similarly, the frequency with which a driver checks his rear-view mirrors before initiating the lane change affects the likelihood of the accident. We can also have an intuition that side-sweep accidents are more likely if there is a significant difference in the vehicle velocities between the current and the target lanes. There are also factors that can reduce the likelihood of the accident: for instance, the signaling of the lane change by the driver can alert the nearby vehicles about the lane change, and they can change their behaviors to give way to the lane changing vehicle. The emerging technology of vehicle-to-vehicle communication offers promising new avenues to avoid such collisions by making vehicles communicate the lane change intent and their positions, such that automatic action can be taken to avoid the accident.

This chapter, discusses the importance of simulating the traffic scenarios leading to a side-sweep accident. We discuss the importance of reducing side-sweep accidents, the impact of driver awareness. We describe the problem statement of the dissertation, the challenges we faced, our contributions and finally, the applications that might benefit from the simulation architecture created.

Objectives
Accidents on highways are one of the major sources of loss of life in modern society. Driving a vehicle on a busy multilane highway while attempting to change lanes occasionally can be hazardous.
That is because due to the blind-spots, the drivers may not see the vehicles in the adjoining lanes.
Significantly more lives have been lost in highway accidents than in terrorism or wars. Part of the reason for this is that Americans spend a significant time on the roads-the average commute is 30 min (NHTSA) [LK94].
Every vehicle has a blind-spot. Some of the contribution factors are: improperly adjusted side and rearview mirrors, the dimensions and the configurations of the side and rearview mirror and the rear and front windshields. Any technology that makes driving safer, faster or cheaper would have a significant human and commercial impact. Whereas, in recent years, significant research focus (and press coverage) has been dedicated to self-driving vehicles, there is no imminent transition to fully self-driving infrastructure. In fact, fully autonomous vehicles are not legal for highway driving-they require human supervision. Even if fully autonomous vehicles become commercially available in the next decade, the vast majority of vehicles on the highway will remain human-controlled, with the mix of vehicles changing only gradually towards more autonomy.
What we are going to see, in the foreseeable future, is a mix of vehicles with various sensing, actuation and communication technologies, and various degrees of automation based on these. Just like with today's cars, drivers will have a choice of turning on or off these technologies, as well as reacting or not reacting to messages coming from them.
It is quite possible that this proliferation of technology mixtures will make driving in the following decades even more unpredictable and cognitively challenging than in the current situation, where uncertainty arises only from the driver's behavior. In the real world this is a very tedious task to accomplish. For example, data published by the National Highway Traffic Safety Administration (NHTSA) [LOW04] is based on the observation of one hundred drivers' (100) road experiences during a 12-month period of time, but this sample is too small order to formulate a standard set of rules.
To understand the traffic of the future, the only feasible approach is to study it through the means of simulation [YQY11, KGN12, QHC13, GW13, KLY12, LJL14, BB16a]. When conducting experiments on real systems would be impossible or impractical where it is possible in simlation. In conducting simulations [SGB05,GWA10,AT13], it is possible to generate a large amount of data with several permutations and combinations with respect to multiple attributes for analytical purposes.
In order to understand the impact of a new technology, we need to study it through a simulation that models not only the technology itself, but the overall environment, visibility, traffic structure as well as the cognitive state, reaction time and so on of the drivers. However, It is not enough to model where a vehicle is and how fast it moves. We need to model the surrounding vehicles, what each of the drivers know, their decision making processes, and their low-tech and high-tech means to communicate with each other.
Furthermore, it is imperative to understand the impact due to the magnitude of the blind-spot in correlation with other attributes such as vehicle density, velocity, frequency of the driver looks before lane change (driver update time) and the percentage of the number of drivers look before changing the lane. In order to define the uniform set of rules, it is vital to collect a large number of statistics and all these attributes need to be taken into consideration. Let us consider the case of side-sweep accidents, which will be the running subject of the remainder of this dissertation.
Overall, we conclude that side-sweep accidents can have complex sources. Technological solutions, such as sensor improvements and V2V communication can improve several steps of this process, but they will always need to be seen as playing a specific part of a system. Our objective in this dissertation is to present the design of a simulator that takes into account all the factors of the lane change scenario. We will then use this simulator to study the potential of technological solutions such as V2V communication in reducing the frequency of the side-sweep accidents.

The Physical and Cognitive Context of a Lane Change
Why do side-sweep accidents occur? Side-sweep accidents occur during driving on multi-lane highways when a vehicle initiates a lane change, a follower vehicle is blocking the other lane.
In the simplest approximation, the cause of the accident is lack of awareness-the driver of the lane changing vehicle is not aware that there is a blocking vehicle in the other lane [YEM95,RT00]. This lack of awareness might be due to either the driver neglecting to check the appropriate side mirror, or the blocking vehicle was in the driver's blind spot. The accidents might also be due to a misprediction of the driver that the blocking vehicle would be safely behind it at the time of the lane. The cause of the side-sweep accident is the incorrect decision made by the driver of the lane changing vehicle, due to lack of information: the driver did not know about the blocking vehicle. This misprediction might be due to a misjudgment of the relative speed of the vehicles, or the blocking vehicle had accelerated since the most recent driver lookout. Driver fatigue, environmental conditions such as icy and wet roads are also contribution factors.
To avoid side-sweep crashes, drivers are instructed to ensure that there is no blocking vehicle by visual inspection, looking both through the rear view and side mirrors as well as turning his or her head in the direction of the target lane [Kni12]. Moving towards more complex causes, a possible cause of the side-sweep accident might be a a failure of communication. Even for drivers who do check the mirrors, a significant number of drivers do not make a strong effort to turn their head and make a visual inspection. As many vehicles have significant blind spots, it is possible that the blocking vehicle exists even if it does not show up in the mirror [MG08].
The size of the blind spot depends on the geometry of the vehicle, the size and adjustment of the mirrors, as well as other means of inspecting the target lane (such as side-view cameras or blind spot sensors).
Another factor comes into the picture when we consider that conditions might change between the last time a driver looked at the target lane versus when the lane change is initiated. For instance, an accelerating vehicle might enter into the blocking zone without the driver being aware of it.
The ability to accurately assess whether the lane will be free at the moment of the initiation of the lane change requires that the driver makes a prediction of the way that the traffic will evolve. This prediction might be impaired in conditions of poor visibility and drowsiness [BH08,HEF10]. Visibility can be impacted by the factors such as atmospheric conditions and the blind spots [KI08].
Enhancing driver attention and minimizing the size of the blind spots of cars can help overcome lack of visibility issues [KKA12]. Another way to monitor drowsiness is proposed by integrating intelligent control systems into vehicles to include the human driver control loop [BH08].
Finally, the cause of the accident might be an incorrect mental modeling. The lane changing driver might judge that the blocking vehicle's driver will slow down to give way, but instead, that driver chooses to accelerate, relying on the lane changing driver see him and abandoning the action.

Reducing the Number of Side-Sweep Accidents
There are several ways in which the number of side-sweep accidents can be reduced [HXX04,RRE13,HS12,BB16a]. Early warning systems improve the driver's awareness of potential blocking vehicles or obstacles [LCF12]. Systems such as side detection sensors recognizing objects on either side of the vehicle and alert drivers of the presence of vehicles during lane changes to avoid side-sweep accidents [SHT12,Car05,DDS14]. For these systems, it remains the responsibility of the driver to make a correct decision, such as canceling the lane change.
Another class of systems, early intervention systems, provide limited automatic assistance to the driver by intervening even after the decision has been made [CC09, RZF10, ARR08, ANL07, YCW09, YCL08,Lee11]. This assistance may be in the form of slowing the vehicle to a stop and/or controlling steering to help the driver stay in the proper lane.
Another important class of methods for reducing the number of side-sweep accidents is by improving the coordination and communication between drivers. This communication does not necessarily need to be mediated through technology. By using its turning lights, the driver can communicate his intention to change lanes to the following vehicles. Following vehicles can also infer this intention from implicit means from the behavior of the driver. Communication in the other direction is also possible: the blocking vehicle might warn the driver initiating a dangerous lane change by honking.
These communication methods might not always work. The fault may be with the initiator (neglecting to use or deploying the lane change signals too late), or with the fault of the receiver (not noticing or ignoring the lane change signal). Even if communication was successfully received, this may not be a clear agreement for the procedure to follow. The signaling driver might expect that the following driver might slow down and give way upon receipt of the signal, while the driver of that vehicle might choose to accelerate instead.
V2V communications are novel networking technologies that extend traditional means of communication between vehicles. V2V technologies might enable many novel driving behaviors such as convoy formation [KB05]. For the purpose of this research, we will restrict our attention to communication between the lane changing and the follower vehicle. The first advantage of V2V communication is that it allows more information to be transferred than the single-bit turning signals. Furthermore, V2V communication can ensure that a transmission has been received by the destination vehicle (although this might not guarantee that the driver of the vehicle receives and also understands the signal).
As with any communication technologies, V2V communication does not guarantee that the appropriate actions for the avoidance of the accident will be made. To perform lane changes safely and quickly, ideally coordinated action from both the lane changing and the following vehicle is needed. However, as new technologies are adapted by drivers gradually, it is likely that, in the foreseeable future in most encounters, only one of the vehicles will be augmented with automated response technology. As we shall see in our experiments, even this might improve the accident rate.

Driver Awareness
Most accidents occur because the driver was not aware of the obstacle in the immediate vicinity due to poor visibility and drowsiness [BH08,Rec,HEF10,HEF10]. Visibility can be impacted by factors such as atmospheric conditions and blind spots [KI08]. Enhancing the driver attention and minimizing the size of the blind spots of cars might overcome lack of visibility issues [KKA12].
Driver drowsiness can be monitored by integrating intelligent control systems into vehicle to include the human-driver control loop [BH08].
Traditional Means of Awareness: The traditional way to be aware of another vehicle in the adjoining lane is the use the side-view (left and right) mirrors, rearview mirror and visual inspection by turning the head and looking back [Kni12]. Properly adjusted mirrors are vital to enhance the angle of view thereby reducing the blind-spot and providing the driver with a better view of the other vehicles before changing the lane. The problem with the traditional means of awareness is that there is no room for errors as there is no early warning system, if the driver make a mistake by not seen the other vehicle because of the blind-spot [MG08]. Collision warning systems use side detection sensors to recognize objects on either side of the vehicle and to alert drivers the presence of vehicles during lane changes to avoid side-sweep accidents [SHT12]. Collision intervention systems go beyond collision warning by providing limited automatic assistance to the driver during potential crash situations. This assistance may be in the form of slowing the vehicle to a stop and or controlling steering to help the driver to stay in the proper lane. However, for the foreseeable future, the main decision maker will remain the human driver.

Problem Statement
The problem addressed in this research is primarily focuses on side-sweep accidents with the objective of developing a framework through which we can evaluate the safety of vehicles in lane change situations. In this research, first, we focus on the visual inspection by the driver through the rear and side view mirrors (the old fashion way), next the benefits of signaling before lane changing and finally, the effect of novel technology, in particular V2V, on the frequency of sidesweep accidents. In order to obtain realistic and useful results, the simulator needs to satisfy a number of requirements. Finally, the simulator needs to model driver behavior [LTB15], in particular not only whether the driver can see the blocking vehicle, but also whether it will check its mirrors, as well as the temporal relationship of the decision making with regards to the sighting.
There is special interest in the ways in which the attributes (itemized list given below) affects the information that reaches the driver and the way this information impacts the decisions of the driver.
• Relative velocity Most of the literature in this domain focuses on automobile blind-spots with respect to sidesweep accidents but do not address the above stated attributes. In this research, however, focus is to comprise these attributes in the modeling framework to mimic the real world scenarios and observe the outcome.

Challenges
There are several challenges simulating the automobile blind-spot simulation with respect to sidesweep accidents and incorporating vehicle conditions in the simulation can be tedious and challenging. Some of the challenges are discussed below.  Some of the research have already addressed some subsets of the problem. According to NHTSA findings, 83% of the single lane changes with a mean duration time of 6.28 seconds. However, these findings does not comprise the relative velocity or the vehicle density because these two attributes can impact at the same time during the lane change. Side-sweep accidents account about 4 to 10 percent of crashes according to transportation researchers. However, the research does not provide a detail breakdown; therefore, it is a tedious factor for statistical comparison among the simulated and the real world data.

Contributions
The contributions presented in this dissertation are enumerated below: The contributions presented in this dissertation are enumerated below: • Develop a simulator to simulate and evaluate the automobile blind-spots in side-sweep accidents.
• Simulate in detail the situational awareness and behavior of the driver, including the visibility with respect to the blind-spots of the vehicle as well as the times of checking the mirrors and initiating a lane change impacting the side-sweep accidents.
• Estimate the time it takes to change a lane and the frequency of side-sweep accidents in various conditions such as traffic density and relative velocity of vehicles.
• Investigate the impact of the spatial dimensions and angle of orientation of automobile blindspots during side-sweep accidents.
• Study the position of the blind-spots, which depends on the geometry of the windows and mirrors of the vehicle.
• Understand the correlation between the magnitude and the orientation of the blind-spots and the frequency of side-sweep accidents in various conditions (e.g., traffic density and relative velocity).
• Investigate the impact of using the turning signals before changing the lane on the frequency of side-sweep accidents.
• Investigate the benefits of V2V communication when changing the lane on the frequency of side-sweep accidents.

Applications
Applications that might benefit from the blind-spot simulation system are listed below: • Education and training: The simulation model can be used for education and training purposes. For example, the areas of interest is to perform what-if analysis of simulating blind spots while calibrating the simulation model for various attributes such as driver lookup frequency, duration between the lookups, vehicle velocity, side and rear view mirror angles and closely mimicking the actions taken by the drivers or simulator operators.
• Identifying learner behavior: In such education and training scenarios, the simulation model can be employed to identify when and where the learner behavior is deviating from the normal or acceptable behavior. The discrepancies can be used to evaluate the performance of the learner. After recalibrating the simulation model can provide important feedback to the driver such that the weaknesses or mistakes can be addressed.
• Traffic school: In addition to normal education and training, the simulation model can be used to train and educate the drivers after traffic violations to reduce the side-sweep accidents.
• Evaluating lane change time: Using the simulation model, we can estimate the time it takes to change a lane and the frequency of side-sweep accidents in various conditions such as traffic density and relative velocity of vehicles.

Outline
The remainder of this dissertation is organized as follows.
Chapter 2 contains a comprehensive literature review of related research.
Chapter 3 discusses the vehicle simulation model as applied to mirrors and the presence and position of blind spots, scenarios of turn signals and V2V communications.
Chapter 4 discusses the information gathering behavior of human drivers, i.e. the ways in which they collect information about the surrounding traffic before they make a decision about a lane change and describes a model of the algorithm human drivers use to decide about a lane change.
Chapter 5 discusses and analyses the results of the simulation studies.
Chapter 6, the conclusions of the study and presentation of future works.

CHAPTER 2: LITERATURE REVIEW
This chapter describes much of the related work that has been conducted on side-sweep accidents with respect to blind-spots, and it identifies the pertinent areas that need more research. Although the literature review is comprehensive in identifying the areas relevant to the side-sweep accidents such as simulator architecture framework, driver lookout statistics, driver awareness about the vicinity, vehicle blind-spot as observed by the driver and technological means of awareness of the blind-spots. But all these research needs are not addressed by this dissertation.

What are the Side Sweep Accidents?
Basically, a side-sweep accident is, when there are two-vehicles travel parallel to each other and because of the blind-spots one driver is unaware of the other while attempting to change the lane.
A blind spot in a vehicle is an area around the vehicle that cannot be directly observed by the driver while in control of the steering wheel, under existing circumstances [LB10, Pla06, JKI00].
Lane changes can occur for a variety of reasons. For example, attempt to overtake the slower vehicle in the front; permit the vehicle approaching faster from behind or simply wants to exit from the highway. Side-sweep accidents can occur for several reasons. For example, [OLW02] suggests that the driver did not see or was unaware of the presence of another vehicle or crash hazard lanes. According to [Kni93], 75% of lane change and merge crashes involve a recognition failure by the driver. Another factor is the driver impairment due to alcohol [RT00] or mental condition [WLL98,MME09] and road conditions at the time of the accident [MME09]. Furthermore, not adjusting the rear view mirror also contributes to the size of the blind-spots resulting side-sweep accidents [Pla06].

Importance of Reducing Side-Sweep Accidents
To enhance the vehicle safety factors, it is imperative to study the correlation between the vehicle blind-spots and side-sweep accidents, which account for 4 to 10 percent of crashes according to  [FLK09]. Therefore, the best solution is to make aware of the blind-spots with respect to side-sweep accidents, thereby, the driver can make an informed decision before changing the lane.

Importance of Side-Sweep Accident Simulation
Modeling and simulation comes into play an important role in side-sweep accidents [YQY11, KGN12, QHC13, GW13, KLY12]. Simulation is performed, when conducting experiments on real systems would be impossible or impractical. Furthermore, conducting simulations [SGB05,GWA10], it is possible to generate large amount of data with several permutations and combinations with respect to multiple attributes for analytical purposes.
The architecture framework of the simulator is based on Object Oriented concepts, and each vehicle is an object. Simulation model is dynamically designed. For example, attributes such as side, rear and front view angles, relative velocity and vehicle density can be dynamically changed.The simulator models in detail the situational awareness and behavior of the driver, including the visibility, windows, mirrors, and blind spots of the vehicle as well as the times of checking the mirrors and initiating a lane change [BB15,BB16b]. The simulator is designed with proper scales of dimensions with respect to the road infrastructure and a variable medium size passenger vehicles [xxxb, xxxa]. Improving the accuracy of microscopic highway simulation through agent based modeling of the conscious aspect of the driver behavior is discussed by [LB10]. The importance of combination of driving simulation and computer modeling is the development direction of accident analysis [KLY12], can provide the necessary basis for conditions by the in-depth data analysis of traffic accidents. The advantage of linear and continuous model of car following model is discussed by [KGN12] and based on the relative distance and relative acceleration of each instant, the simulation model predicts the future behavior of the leader vehicle and according to this behavior, the acceleration of the follower vehicle is controlled. Reconstruct side pole impact accidents by computer simulation is discussed by [QHC13]. In this method, first the motion of the vehicle before impact is reconstructed, and then construct the impact between the vehicle and the pole, these two steps is repeated to obtain a reasonable simulation results.

Driver Awareness of Vicinity and Blind-Spots Avoidance
As discussed [BH08,Rec,HEF10], most accidents occur because the driver was not aware of the obstacle in the immediate vicinity area due to poor visibility and drowsiness [HEF10]. Driver awareness about the immediate vicinity can be either traditional or technological means or both.
As discussed by [Kni12], the traditional way is to properly adjust the side and rear view mirrors and [LJJ11, LCF12] discussed a vision-based real-time gaze zone estimator based on a driver's head orientation and this method can helps during the day as well as night times. While overtaking and changing the lane can be a risk; therefore, [MG08] propose an aid system based on image processing to help the driver in these situations [RRE13,CC09]. [ANL07, RZF10, YCW09] discusses the inter-vehicle cooperation based on wireless mobile ad hoc network while changing the lanes in order to avoid or reduce the side-sweep accidents. [BH08] discusses the controlling drowsiness of the driver by integrating intelligent control systems into vehicle to include the human-driver control loop. In order to overcome the visibility issues because of the shadows, occusions and poor light conditions; video image detector method is proposed by [Lee11]. [SHT12,JKI00] proposed collision avoidance system be installed on vehicle's rear-end window of preceding vehicle.
In order to avoid intersection accidents, the situation analysis method for driver assistance is proposed by [HS12]. [KI08] proposed, adjusting side-view mirrors dynamically in order to enhance the visibility when the driver is in the process of changing the lane.   In this chapter, the blind-spots orientations, simulation model architecture and data sets are described which were used to simulate the automobile blind-spots presented in this thesis. As established in Chapter 1, the areas of interest are to perform what-if analysis of simulating blindspots while calibrating the simulation model for various attributes such as driver lookup frequency, duration between the lookups, vehicle velocity, side and rear view mirror angles, and closely mimicking the actions taken by the drivers or simulator operators. In Section 3.1 orientation and the spatial dimensions of the blind-spots are described. In Section 3.2 simulation model architecture is described.

Lane Changing Times
In Section 3.3 the model input parameters are introduced, and the output is discussed. In Section 3.4 the driver status model described. In this section, there is an elaboration on how does the driver acquire the information, and how does it impact the behavior of cautious human drivers.
Lastly, in Section 3.6, there is an elaboration on the possible scenarios of vehicle collisions and finally, in Section 3.7 the lane change algorithm is presented.

Blind Spot Spatial Dimensions And Orientation
The simulation model assumes that the vehicle always is in the middle lane heading from left to right and there are blind spots on the left and right sides as well as the back side of the car.        On the other hand, a combination of properly adjusted and a larger rearview mirror and/or rearview window can contract the size of the blind-spot. D1 is the distance of eye-point of the driver to the center of the vehicle; d2 is the distance of eye-point of the driver to the rear view mirror, and d3 is the distance of eye-point of the driver to the rear view window.

Model Architecture
We designed the UCF LCS to model in detail the events immediately preceding a lane change.
Most traffic simulators take the perspective of the overall highway, and are interested in the overall traffic metrics such as throughput, average speed, average time to destination, and the evolution of the traffic over timespans of hours. The UCF LCS, in contrast, is only interested in the vehicles in close proximity to the lane changing vehicle and a short timespan of tens of seconds necessary for the lane change maneuver. We shall need to ensure that the traffic near the lane changing vehicle is modeled realistically. We are not interested, however, in the vehicles before they enter and after they leave the zone of the lane changing vehicle.
The simulator had been implemented in Java, and it has been designed such that every aspect of the traffic model, vehicle geometry and road geometry can be specified in parameters. For the simulation study described in this paper, we fixed the road and overall vehicle geometry to correspond to average sizes of US highways and a mid-size four door vehicle, respectively. We retained these as default parameters, however, the mirror adjustments can be done.
Photorealistic visualization was not part of the objectives while designing our simulator. We found, however, that a simple graphic rendering can help us understand the various scenarios. Figures 3.4, 3.5, 3.6 and 3.7 show a series of screenshots from the '"warm up" phase of the scenario.
In general, it is difficult to initialize a traffic simulator to a random point in the traffic, as in normal traffic the position of every vehicle is determined by its history of interactions with other vehicles.
Thus, like many simulators, LCS uses a "cold start" approach. It starts with an empty highway and a specific arrival rate of the vehicles (in our case, Poisson arrival with a specific average cars per minute) and lets the dynamic interactions between the vehicles stabilize. The '"measured part" of the simulation, in our case the lane change intent, will happen after the traffic has stabilized. This warm up process is shown in the screenshots in Figures 3.4, 3.5, 3.6 and 3.7.
The architecture framework of the simulator is based on Object Oriented concepts, and each vehicle is an object [BB15]. Simulation model is dynamically designed. Meaning the configura-

Model Input Parameters
In the following, we report the results of several simulations of the scenarios described in Section 3.5. There were several types of simulations being performed to derive the mean averages.
Each experiment was run with 300 iterations. On Average, 15 cars per minute (CPM) or 900 cars for an hour were ran. After each iteration, the experiment initialization variables were reset to their initial values before the next iteration. After iterations for each experiment, the data for each iteration was derived to calculate the averages. The figures' plots are based on these averages.
At the start of the simulations, the simulator was initialized with default parameters as stated in Table 3.1. The simulation was run for 30 s; this was the ramp-up period. Afterwards, this period and during the steady state environment, data was collected with respect to several data points as discussed in the following section.   • The strategic level: This is the highest or the conceptual level where general goals such as route choice, navigation, and timing are set.
• The tactical level: The driver maneuvers at tactical level, with particular focus on how context information influences or reacts driver's performance. Also, the tactical level involves decision making related to the management of current driving activity (e.g., maneuvering). Pertaining to the simulation, there are three types of simulation models: Driver Attention, Driver Visibility and Driver Decision Mental models.

Driver Attention Model
This model facilitates us to model how often or the frequency of a driver looks into the rear view mirror, side view mirrors, or forward before changing lanes. As displayed in Figure 3.  .9, depicts the state transition diagram where the consideration of the system, which may be described as being any one of set N distinguished states where a state could be in S1 to S6. States S1, S2, S3 and S4 are already discussed and S5 represents the state after a successful lane change; however, S6 represents the state where a crash can occur. Competent or a prudent driver may decide to change the lane at state S2 as indicated by an arrow; however, there could be a higher probability of an accident compared to the lane change at state S3. This is an example for a person who looks less frequently or did not see a vehicle in the adjoining lane due to a larger blind spot. Also, Figure 3.9 represents all the possible scenarios that can occur among the states. For example, the driver may decide not to look again and instead may decide to change lanes abruptly.
This scenario is reflected as a move directly from states S2 and S3, however, state S5 with a lower probability of a successful lane change at state S2 compared to state S3. A move from S2 to S6 where a higher probability of a crash, compared to a move from S3 to S6. Loopback arrows, for example S1, represent when the driver decides to stay instead of changing lanes and the arrow from S1 to S2 represents moving to the next state.

Driver Decision Mental Model
Driver mentality is based on the driver competency, environmental conditions and the traffic con-  Figures 3.8 and 3.9 already displayed the events that could occur with respect to a driver's thought process when a vehicle travels in the middle of a highway while attempting to change from middle to the left lane.

Drvier Scenarios
Before we proceed further, we need to clarify what a "crash" situation means in our simulations.
While we do model the conscious behavior of the drivers, we cannot realistically model reactive actions that take place at time scales of milliseconds, such as sudden evasive actions, where several inches in the position of the vehicle might make the difference. We will say that if a vehicle initiates a lane change while another vehicle blocks the lane, we will count this as a '"potential crash situation". In practice, it is possible that the crash will be avoided through a quick evasive action by the follower vehicle or a last second cancellation of the lane change by the driver. While the number of the near-crashes will be overall higher than the actual crashes, whether a given encounter ends up in a crash or near-crash is primarily a probabilistic event. Our models will predict the potential crash situations, and thus the number of real crashes will be proportionally a smaller fraction of these.  vehicle is either blocking the lane or, based on its current speed, will be blocking the lane at the moment of the lane change, the driver will wait. Note that for Scenario 1 and 2 we did not assume any action taken by the driver of the following or blocking vehicle, as this driver is not aware of the intention of changing lanes.
The average driver might still get into crash situations. One reason might be that it missed checking the mirrors (according to the statistics, the drivers check them only 83% of the time).
Another reason might be that the driver might have checked the mirror at a moment when the blocking vehicle was in a blind spot and the driver did not track the vehicle from a previous sighting.
Scenario 3-Driver using turn signals: In this scenario, the behavior of the driver from scenario 2 is augmented with the fact that the driver uses the turn signal before changing lanes.
While in previous cases the drivers of the following vehicles were not active participants, in order to model this scenario, we need to know the action taken by the driver of the following vehicle.
There are several important parameters of this scenario. The first one is whether the follower driver took action upon seeing the signal. The normal action to take upon seeing the signal is a moderately strong braking. This action might not be taken, either because the driver did not see the Second, we expect that, on average, the time to change lanes will be reduced, since, by the action of the following driver, the lane changing driver might find a slot into which he/she could change lanes more quickly. For the same reasons as before, there is no guarantee that there is a no crash situation even in the case of the use of the turn signal. Let us consider what we expect such a system to achieve. First, the system should obtain better information about the status of the target lane than visual inspection by the driver. The V2V system will listen all the time and blind spots will not prevent the communication. Furthermore, by directly transmitting the velocity of the following vehicle, the overall system can have better information about the relative speeds. In general, it is not easy for drivers to estimate the relative speeds of the vehicles from short glances in the mirror.
Nevertheless, equipping a vehicle with this technology will not immediately mitigate all possibilities for a side-sweep accident. To begin with, not all vehicles will be immediately equipped with this technology. Second, V2V communication is limited by the transmission range of the V2V radios, which can be further limited by environmental conditions. If the following vehicle is significantly faster than the lane changing one, and the transmission range is small, it might happen that the V2V-based notification arrives too late to prevent a side-sweep collision.
Note that many other possible scenarios exist. V2V communication can be also implemented with an intelligent decision making factor in the follower vehicle instead of the lane changing one.
In another scenario, we can assume the existence of an intelligent agent in both vehicles, which might negotiate a coordinated course of actions. Multi-vehicle coordinated action using vehicle-to-infrastructure (V2I) communication is also a possibility. Exploring these and similar scenarios, however, is beyond the scope of this research.

Lane Change Algorithm
The lane change algorithm discussed in this research is applicable to a cautious driver and not for a    [LOW04]. The frequency of checking times depends on the attributes such as traffic and environmental conditions with a minimum of two instances before deciding to change lanes Figure 3

Scenario Observations
The impact on side-sweep accidents due to relative velocity and vehicle density 1. Observe the correlation between the number of side-sweep accidents with respect to the relative velocity. 2. Observe the correlation between the number of side-sweep accidents with respect to vehicle density.

Angle Of Orientation
The Table 4.2 provides itemized statements are some of the highlights of the data acquisitions of the model output parameters on angle of orientation of side-sweep Accidents.  Orientation of the blind spot 1. Orientation or the shape of the BS is based on the side and rearview mirrors adjustments. 2. Evaluated the various attributes such as successful, unsuccessful, number of stay-back before l ane change. The duration of the stay-back could affect because of relative velocity and congestions.
There are several types of simulations being performed to derive the mean averages and then to observe outputs. Model output is categorized into three areas: In this experiment, we studied the impact of successful lane change solely based on the visual inspection.
• Simulating the impact of blind-spots on the frequency of side-sweep accidents • The automobile blind-spots' spatial dimensions and angle of orientation on side-sweep accidents In this experiment, we studied the impact of the presence of the turning signal to the success of the lane change.
• The impact of the turn signal duration • The rate of successful lane changes as a function of the driver wait time.
In this experiment, we studied the impact of the presence of the vehicle-to-vehicle communication with respect to the success of the lane change.
• The impact of V2V transmission range and relative velocity • The line of view of the driver with the left mirror improperly adjusted.
• The Impact of the Blind Spot When V2V Communication Is Present

Correlation between the number of successful and unsuccessful
As shown, accident rate is approximately 2% to 3% until the relative velocity approaches 3 mph and increases sharply along with the velocity and vehicle density.

Angle of Orientation of Side-Sweep Accidents
This section discusses the impact of the spatial dimensions and angle of orientation of automobile blind spots during side-sweep accidents. Using this simulator, a study was performed to evaluate the correlation between the magnitude and the orientation of the blind spots, the frequency of sidesweep accidents in various conditions (e.g., traffic density and relative velocity) and time to change the lane. All the test scenarios discussed in this section, the left angle size varies from 5 • to 25 • , relative velocity is 1 mph, duration between looks 8 sec, driver lookout frequency probability was set to 0.83.

Lane Changing Scenario2: Observations as a Function of Relative Velocity and Cars per Minute
This section includes the several simulation outputs with respect to lane changes as a function of cars per minutes (CPM) and relative velocity. As already stated, CPM and relative velocity were initially set to 5 and 1 mph respectively. The simulation was performed while incrementing CPM by 5 and until 25 and the relative velocity by 1 mph until 5 mph. The simulation was repeated with respective inputs, and the results were recorded.  (2) the distance between the vehicles. Suppose the first or the front vehicle velocity (v1) is 100 feet/second, and the second vehicle velocity (v2) coming from behind is 130 feet/second, then, the relative velocity is -30 feet/second (v1 -v2), and the distance between the two vehicles is 60 feet, then, it takes 2 seconds for the collision to occur.   Figure 5.10, this can be because of a higher relative velocity; the driver tends to be more cautious and takes more time and looks more frequently before deciding to change the lanes. Moreover, further analysis revealed that the number of times stayed back and time to change lane as much as two times if the relative velocity is 3 mph compared to 1 mph respectively. This is because of several reasons: • at a higher velocity, the drivers maintain a longer distance between the cars, • the driver tends to look more frequently, • thereby, number of checks before lane change is higher, and   change the lane with respect to relative velocity and blind spot angle. In the first analysis, the blind spot angle scales from 5 • to 25 • and relative velocity is set to 1 mph and the second analysis, the relative velocity scales from 1 mph to 5 mph and blind spot angle is set to 5 • and compare the simulation output. In both the analysis, the CPM is set to 25.
Blind spot left angle scales from 5 • to 25 • , relative velocity scales from 1 to 5 mph, cars per minute 25, duration between looks 8 sec, driver lookout frequency probability is 0.83. The attribute attempts represents the number of times the driver attempts to change lane.

The Impact of the Turning Signal
In this experiment, we studied the impact of the presence of the turning signal to the success of the lane change. The simulation parameters are described in Table 5.2. There are two scenarios are discussed: • The Impact of the percentage of using the turning signal In general, drivers are advised to signal "ahead of time", that is, to allow the signal to be on for a certain amount of time before initiating a lane change. To verify this shows the parameters for the simulation. These largely echo the ones from the previous experiment, with the probability of turn signal use set to 90%.  We observe that this increase is higher than what we would expect from the very small percentage of the drivers who slow down when seeing the signal.

Vehicle to Vehicle Communication
In this set of simulation studies, we consider the case described in Scenario 4, which is a case of V2V communication where the following vehicle communicates its location and velocity to the lane changing vehicle. Table 5.4 lists the common simulation parameters for the simulation studies in this section.

The impact of V2V transmission range and relative velocity
In the first set of experiments, we varied both the relative velocity and the V2V transmission range. Figure 5.17A shows the experimental parameters for these simulation runs. The relative velocities had been studied for 1-5 mph faster compared to the current lane.       Smaller standard deviation and variance values indicate that the data points tend to be very close to the mean. In other words, by maintaining the proper distance between the two vehicles, it is possible to reduce the side-sweep accidents at a higher relative velocity.

The impact of the blind spot when V2V communication is present
One of the major sources of side sweep accidents is the presence and size of blind spots. While drivers can check the presence of a vehicle in a blind spot by turning in the appropriate direction but in general a lot more drivers fail to do so compared to those who that fail to check the mirror. This problem is magnified when the mirrors are improperly adjusted (see Figure 5.19). This creates larger-than-optimal blind spots (compare with Figure 3.1 which shows the optimal adjustment).    Another known cause of side sweep accidents is the lack of attention of the driver. A measure of this value is the probability of the driver looking to the left when initiating a lane change. In previous work [BB16b] we found this impact to be quite significant. In this research, the lane changing scenarios were analyzed with constant vehicle velocity. In future research, the observations and the results obtained from the simulations pertaining to the scenarios with respect to variable velocities and the impact of the spatial dimensions of the automobile blind spots on the side-sweep accidents will be analyzed.

The impact of driver attention when V2V communication is present
In the following section, we analyzed and produced the results of these scenarios and provided a comparative analysis of the results with respect to traditional and non traditional communication technologies.

Traditional Communication -Without Using the Turn Signals
We found that some of the experimental results are comparable with the National High-     Some other observations and conclusions: * As based on the output of Figure 5.5, it can be observed and considered that 16 • angle is a demarcation point regardless of the size of the blind spot angle and CPM at relative velocity 1 mph. The percentage of successful lane changes will begin to diminish more significantly. If the blind spot angle is greater than 20 • . * As observed in Figure 5.6, the unsuccessful lane change percentages are somewhat similar when the angle is approximately 5 • , but they increase rapidly along with the increasing blind spot angle and vehicle congestion. * As displays in Figure 5.7, the driver waiting time and lane changing patterns are very similar regardless of the size of the blind spot angle and the CPM. Also, the average wait time to change the lane gradually decreases as the the blind spot angle increases. That is because of a larger blind spot, the driver may not see many vehicles and tends to assumes that there are no other vehicles. * As displays in Figure 5.9, because of the higher relative velocity, the time to change the lane increases exponentially, resulting in a longer period to change the lane safely.

Traditional Communication -Using the Turn Signals
According to the simulation results, we can conclude that side-sweep accidents can be minimized, if the higher percentage of the drivers use the turn signal few seconds before initiating of a lane change. Table 5

Non Traditional Communication -Using the V2V Communication Technologies
In conventional systems such as unidirectional communications, signaling before changing lanes, the Driver is in control as opposed to the Car. Ideally, based on the simulations results, the combination of the two systems (unidirectional and bidirectional) the side-sweep accidents can be eliminated and enhance the driver safety. Based on these results, it can be concluded, vehicle-tovehicle communication can enhance the driver safety during side-sweep accidents and it can be enhanced further in conjunction with higher frequency and duration of the using turn signals.