**4. The overtaking behavior with the combination of HDVs and CAVs**

The final section of this chapter focuses on providing an in-depth analysis of a complex scenario in which the autonomous vehicle (AV) has to perform maneuvers in the presence of HDVs. Upon a cursory glance at the current state of AV research [39], it becomes obvious that not a lot of emphasis is being placed on explicitly modelling the varying behavior patterns of HDVs on the road. One such instance that highlights the need to model the varying HDV driving patterns is the car overtaking problem in a bidirectional traffic flow setting. In this problem, a scenario with three vehicles is considered; two HDVs and one AV (ego vehicle), as shown in **Figure 3**. The HDVs are travelling in opposite directions in adjacent lanes and the ego vehicle is following one of the HDVs. The objective of the AV is to safely overtake the vehicle travelling ahead while maintaining safety distances to the HDV in the adjacent lane, the HDV travelling ahead and the boundaries of the road.

This is a particularly hard problem to solve because it involves a scenario composed of both human-driven and autonomous vehicles. The first major complication is the lack of global information because the V2X communication protocols cannot be leveraged in this scenario due to the presence of HDV. Then, there is the problem of uncertainty that arises due to the varying driving patterns so the algorithm needs to be robust enough to handle the different driving patterns of human drivers. Moreover, the traditional Supervised Learning based approaches cannot be applied directly to this problem due to a lack of labeled training data. Finally, the model-free Reinforcement Learning (RL) based techniques [40] cannot be employed due to a lack of safety guarantees while the model-based RL or Control techniques [41] cannot be employed since they require an accurate representation of system model and cannot capture uncertainties that arise due to varying driving patterns well.

In this chapter, a simplified stochastic control based formulation taken from [42] is laid out to provide a mathematical description of the problem. The formulation is followed by a brief discussion of a couple of algorithms to give the readers a glimpse of the possible avenues that could be taken to reach a solution. The references to detailed resources are also provided for the interested readers to explore further.

**Figure 3.** *Overview of the car overtaking problem.*

*3.2.3 Broadcast communication based method*

broadcast message.

communication bandwidth.

*3.2.4 Temporal logic based method*

generated logic.

**3.3 Summary**

**134**

Some of the major issues in coordinating automated vehicles at merge intersections are the problems caused by imperfect communication. In these type of applications, delays of even a few seconds can have devastating results. Work on using Pseudo-perturbation based broadcast communication (PBC) [37] instead of unicast communication focuses on reducing the overhead on the V2I communication systems and leveraging the capabilities of V2V communication to handle any short term changes. In this method, a global controller (infrastructure) broadcasts an identical message to all vehicles in its vicinity. Each of these vehicles uses this information along with V2V data from other vehicles to select a suitable control strategy to safely perform the coordinated merging task. Vehicles send updates back to the central controller and the controller uses this feedback to decide its next

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

The key focus of this research was to extend available PBC capabilities to handle the multi-state vehicle dynamics and the multi-objective optimization required to solve a freeway merging coordination problem. The capability of this system to handle both CAVs as well as a mix of CAVs and human driven vehicles was also showcased. Here, the CAVs are controlled by the coordination system and are shown to be able to function even in the presence of human driven vehicles. The smoothness of the actual merging process was also evaluated through simulation. The main output of this research was to show that complex coordination problems such as freeway merging can be successfully solved with the use of minimal

Disadvantages: Lack of focus on the actual merging algorithm. Based on the broadcast signals, the decisions individual vehicles make may be sub-optimal and cause an unnecessary delay in the system. Also, very little work has been done on

This method involves formalizing traffic rules using temporal logic [38] in order to ensure safety and robustness of automated highway vehicle control. This method helps with formulating existing traffic rules in a mathematical way in order to be easily applied in CAVs. While this method does not solely focus on the merging problem, the merge window is addressed in the metric temporal logic (MTL) formulas included. When the merge operation is formulated as a MTL formula, it leads the way to specifying safety guarantees in autonomous vehicles. Furthermore, the legality of trajectories generated by a motion planner can be easily checked using these MTL formulae. It also allows the use of standard verification and validation methods in order to ensure that there are no loopholes or issues in the

While there are many methods to improve intelligent merging behavior, the core fundamentals of these algorithms are quite similar. They look into minimizing gaps or under-utilized space on the road while minimizing the control inputs (acceleration and braking) needed to achieve this. All algorithms also prioritize safety and fairness in the merging process. Additionally, there are extensions that focus on maximum capacity utilization by moving vehicles already on the highway to less congested lanes, which further improves the efficiency of the merging process. Each of the algorithms discussed in the section has its own advantages and

seeing whether this system actually has a effect on throughput.
