Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

3.1.1. Cooperative Smart Car Parking

The proliferation of vehicular traffic has made searching for a parking space a frustrating task for the driver. The INRIX research survey [17] states that American drivers spend an average of 17 h per year in finding space for parking, leading to a loss of $345 in terms of wasted time, fuel, and CO₂ emission. Difficulties in finding a suitable parking place encountered globally result in traffic congestion and air pollution issues that need to be addressed, and urgently. Readers are highly encouraged to look into the survey conducted by Diaz et al. [18], which extensively investigated the smart parking systems and classified them into six categories: parking reservation systems, parking guidance and information systems, crowdsourcing in intelligent transportation systems, centralized assisted parking search, agent-based guiding systems, and electrical vehicle parking systems. Several other research studies have been conducted to address the car parking issues, and some of the latest solution approaches are highlighted in this section.

Correa et al. [19] implemented a smart parking system on top of the vehicular sensor network considering the heterogeneity of the cars (traditional and autonomous) in the parking slot. The proposed system communicates and assists automated vehicles in finding the best parking place based on collaborative approaches. A designed tree-based searching algorithm (TBSA) is implemented seeking to provide the best parking slot and to enhance the accessibility of the parking for other autonomous cars. The results concluded that the proposed approach can perform best when the autonomous vehicles enter the parking lot in succession. In another study [20], the authors proposed a decentralized parking approach called Cooperative Car Parking (Co-Park) leveraging V2V communication and agents installed on vehicles letting the cars find suitable parking in large areas, such as airports, urban areas, and all. The extensive simulation on the Co-Park approach by comprehensively investigating the various parking scenarios showed that the strategic cooperation among the agents leads to efficient car park searching for all.

Wu et al. [21] addressed the multi-vehicle cooperative parking issues. They implemented the model based on the kinetic model integrated with dynamics, endpoint, and collision avoidance constraints. Based on the Gauss pseudo-spectral method (GPM)-based technique, the authors transformed the planning of the trajectory of the multi-vehicle problem into an optimal control problem. The experimental results demonstrated that the GPM has the potential to effectively solve the cooperative multi-vehicle parking problems. Likewise, authors in Reference [22] focused on the planning and coordination of multi-vehicles in automated parking scenarios. This problem was modeled as symmetric mixed-integer linear optimization aimed to investigate the intentions and driving styles involved in optimization problems.

To sum up, the exhaustive survey conducted on smart parking systems (SPS) by Reference [18] uncovered several open issues and challenges in the SPS, such as:

• the consideration of user preferences while suggesting the optimally available parking spaces to the drivers;

• highlighting that the implementation of SPS should be context-sensitive since all cities have different infrastructures, budgets, and societies; and

• found that less work has been done on rescheduling the routes established by the SPS taking towards the parking slot in case of unexpected event triggers.

3.1.2. Lane Change and Merge

The lane changing and merging shown in Figure 4 demonstrate one of the hazardous maneuvers that lead to one-tenth of all traffic crashes. In America, 33% of all the accidents occurred when vehicles were making a lane change or performing merge maneuvers [23,24].

Cooperative and automated lane change leveraging intelligent driving technologies can substantially enhance road efficiency and safety. The rapid growth of V2V communication has made possible the development of the cooperative automated lane-change maneuvers. Luo et al. [25] contributed to the lane change model by implementing the automated lane change maneuver using V2V communication. Considering the current state of the neighboring vehicles, the model could determine whether to carry on the lane change maneuver or to return to its original lane based on the minimum safety spacing (MSS) model. However, this model was implemented for two-lane settings and was not suitable to work with multi-vehicle lane-changing maneuvers. Predicting the state of the surrounding vehicles, a decentralized cooperative lane merging framework based on coordination strategy is developed by Reference [26] to avoid conflicts. The experimental results showed that the proposed framework could perform well in traffic dynamics. Previous V2V-based approaches did not consider the characteristics and cooperation of nearby vehicles, which results in danger while taking lane-change maneuvers. To conquer these issues, Reference [27] implemented a V2V-based cooperative lane change framework while considering the information of the surrounding vehicles to plan the trajectories. Besides, the trajectory planning is formulated as an optimization problem to increase the safety and comfort in lane changing, considering the constraints of the vehicle dynamics.

Ref. [28] exploited reinforcement learning to implement the cooperative lane change approach for connected vehicles. While setting up the reward function, the proposed study considered two factors: the delay caused by an individual vehicle and traffic efficiency of the road segment. The simulation-based results demonstrated that traffic efficiency is enhanced in congested scenarios. Given the deep learning approaches, Reference [29] implemented the hierarchical Deep Q-Network reinforcement learning-based decision-making framework for lane changing maneuver. The experimental results proved the effectiveness of the proposed architecture in terms of decision-making.

The authors of Reference [30] proposed a coordinated lane merging approach for a vehicle to everything (V2X) scenarios based on a centralized system for connected cars. The application comprised of a traffic orchestrator is developed to provide the trajectory recommendation to the vehicles. Besides, several machine learning algorithms and data analysis algorithms were implemented to determine whether the connected vehicle (CV) carried out the lane merge maneuver successfully. Ref. [31] extended this work by integrating the deep learning reinforcement learning and data analysis algorithms seeking to predict the trajectory recommendations for CV. The results concluded that Dueling Deep Q-Network outperformed the Deep Q-Network in terms of best performance and delivering human-like trajectories.

Generally, the lane merging algorithms consider the simple rules to generate the order of the vehicles for merging. To solve this problem, Reference [32] developed the cooperative merging strategy and modeled it as an optimization problem. The proposed research study applied a genetic algorithm to solve the optimization problem by taking the objective inputs of minimum travel time of vehicles and the maximum number of merging vehicles. The simulation-based experimental results showed that the cooperative lane merging approach proved the ability to improve traffic efficiency and fuel consumption. Several research studies implemented the slot-based (slot: the free area between two vehicles) approach to deal with the cooperative lane merging issues [33,34,35]. The aforesaid approach is comprised of two steps: (i) it determines the merging availability in the target lane; and (ii) the feasibility of actions is considered to find the optimal slot for merging acceleration. This approach makes a decision only based on current states, while ignoring the historical data, which results in failures.

3.1.3. Cooperative Intersection Management

Although the intersections deal with a relatively smart part or road system, the Federal Highway Administration (FHA) reports that 40% of all the accidents occurred at intersections, considering as the second-largest category of accidents [36]. Connected and autonomous vehicles (CAVs) communicate and exchange information with the Intersection Manager (IM) and with other CAVs intending to pass the intersection safely, as shown in Figure 5, whereas the conflict zone shown in the figure represents the area where the collision of the vehicles coming from different lanes can happen. Therefore, automated intersection management systems should be implemented to enhance road efficiency and safety, as well as to minimize fuel consumption and travel time.

Mainly, the intersections are of two types: signalized and non-signalized, which are controlled by V2V and vehicle to infrastructure (V2I). Signalized intersections have traffic lights to boost road safety and efficiency by allowing the vehicles to pass an intersection based on traffic lights, whereas non-signalized intersections are comprised of yield, stop signs, and sometimes with the possibility of having no signs at all. In signalized intersections, the Cooperative Intersection Management (CIM) permits the vehicles to communicate and exchange a variety of information with the infrastructure, such as traffic perception, negotiation to pass the intersection, vehicle signage, and so on, while, in non-signalized intersections, the CIM assists the drivers by providing the global view of intersection managements to make optimal decisions [38]. Some of the recent research studies on signalized and unsignalized intersection management are listed in Table 1.

The presence of traffic lights in signalized intersections has proved efficient traffic flow and safety. A considerable amount of research studies have been added to improve traffic light algorithms. Some of the well-know implemented approaches are fuzzy logic [39,40], neural networks [41,42], mathematical models [43], query theory, and markovian-based models.

Recent studies on a network of signalized intersections are based on max pressure and back pressure, providing algorithms that enable the smart traffic lights. The information of traffic queues and turn probabilities are the important inputs of these algorithms. In Reference [44], the authors implemented an optimization algorithm aiming to minimize the traffic queue length by giving green light to lanes having the largest queue. The traffic information was collected via V2I communications. The authors concluded that wireless communication technologies are more advantageous than the conventional loop detectors since the transmitted information is in more detail. In References [38,53], the authors implemented a V2X architecture, allowing a group of vehicles to negotiate with the controller for the green light. The vehicles estimated the traffic queue length by themselves. While awaiting the green light, vehicles communicated via V2V and grouped automatically based on their destinations. Each group elected a leader to take charge of the queue length. Finally, each leader of a group communicated their queue lengths with the intersection controller to get the green light. In another study, to avoid collision and minimize the waiting time at the intersections, Alouf et al. [45] implemented a model scheduling the AV at an intersection using production line technique and K-Nearest Neighbor algorithm to predict the right-turn of the vehicles. Their simulation-based results concluded that their model outperforms traditional models in terms of random-pattern traffic flow.

The intersections can be modeled as tiles, where the vehicles need to reserve the tiles for their decided routes for a certain time. Once the titles and time slots are approved, the vehicles can cross the intersections based on their reservation time. The authors of Reference [48] studied the cooperative resource reservation with a simple intersection layout and assumed that, once the tile is reserved, the vehicle has to maintain the same spend until the intersection is crossed. The authors compared their simulation-based experimental results with two intersection management approaches, overpass and traffic light. Results showed that their proposed outperforms overpass and was two to three times good than the traffic light method. Following this, in another study, Reference [47], the authors implemented a traffic simulator named ISR-TrafSim to test the reservation-based intersection management systems. Two types of intersections (roundabouts and crossroads) were considered, and different procedures were applied. In the case of roundabouts, vehicles sent the information to the intersection agent to notify their driving plans, whereas, in the case of crossroads, the intersection agents themselves automatically detect the driving intentions of the vehicles. In both cases, the intersection controller was responsible to allocate time slots and space tiles.

Liu et al. [37] implemented a cooperative scheduling mechanism for AV crossing the intersections. The foremost goal of their research was to minimize the delay at unsignalized intersections. Their proposed mechanism is comprised of mainly three phases: firstly, the intersection manager is used as an information collection point, and it prioritized all the vehicles and plans their trajectories. Secondly, a window searching algorithm was used to find an entering window aiming to produce collision-free trajectories. Lastly, based on the velocity profile computed by dynamic programming, each vehicle arranged itself individually to pass through the intersection. Their experimental results concluded that the proposed approach minimizes the evacuation time and improved the throughput by 20%. Similar to this study, the authors of References [49,51] worked on space and time-aware approaches to manage the AV at an intersection that is robust against external disturbances and model mismatches. They proposed that the intersection manager is responsible to assign the Time of Arrival and Velocity of Arrival to an autonomous vehicle in such a way that the route of AV before and inside the intersections does not conflict. On the other side, AV is responsible to track the best route to reach the intersection at the given Time of Arrival and Velocity of Arrival. The algorithm tested on 1/10 scale intersection of AV showed that it achieved 2.7X more throughput as compared to velocity assignment techniques.

Recently, multi-intersection traffic signal control approaches have grabbed the attention of researchers, which are more practical than single-intersection traffic signal control. Ref. [54] implemented the decentralized coordination graph algorithm based on multi-agent deep reinforcement learning and a coordination graph. The proposed algorithm is designed to make traffic control policies by exploiting the current traffic states, the history of observations, and other useful information. The experimental results conducted on real-world scenarios reported that the proposed algorithm outperformed other algorithms in terms of traveling time and delay leading to improved traffic congestion. Refs. [52,55] also implemented the Deep Q-Network (DQN)-based cooperative traffic signal control for multi-intersection. Each intersection was modeled and trained as an agent and attempted to collect the traffic information from the road environment. The extensive results showed that the proposed approach improved traffic congestion significantly at multiple intersections. Some of the other research studies worked on other approaches, i.e., centralized cooperative resource reservation, time-space reservation, trajectory planning, virtual traffic light, and intersection collision avoidance, which are extensively discussed in the studies of References [44,56,57,58].

Despite the rapid and progressive advancements in intersection management systems, we highlight some of the open issues and challenges of CIMs that need more research and advanced solution approaches to overcome these issues. Some of the common challenges are optimized resource utilization, several uncertainties from the control, communication, mechanical perspectives, vulnerable road user detection, sensing and identification of pedestrian and cyclists at non-signalized intersections, communication issues, such as delays packet loss, security, bandwidth limitations, low-quality data, distributed processing of data either at intersection controller or by CAV, and collision avoidance [46,50].

3.2.1. Platoon Leader Election

In many applications, a leader or coordinating vehicle is required to synchronize and accomplish a cooperative goal for a group of vehicles. Some of the applications are intersection coordination, Virtual Traffic Lights (VTL) [61], and highway merging [62], and vehicle platooning is one of them. These applications allow vehicles to communicate in a V2V manner and eventually elect a leader unanimously.

The leader vehicle plays a substantial role in platooning and responsible for the maneuvers, such as formation, merging, and splitting the platoon, by transmitting the information with the following vehicles. Some of the additional roles and responsibilities of the platoon leader are shown in Figure 7.

Besides, the leading vehicle is in charge of defining the acceleration, inter-vehicle distance, and direction for the platoon and shares this information with the trailing vehicles at a constant time interval (t) to maintain the string stability of the platoon [63]. In addition, it is the PL who has to be active constantly, more responsible, aware of situational conditions, control the forward traffic conditions, communicate with the infrastructure, and exchange information with the following vehicles to control and maintain the platooning successfully. Since the platoon leader takes the lead of the whole platoon, consequently, the result of its operations and decisions directly influences the safety, traffic throughput, and stability of the platoon [64]. In platooning, data transmitted by the leading vehicle to the trailing vehicle is processed by the cruise controller and directly affects the speed of the trailing vehicles. In this scenario, electing a PL with malicious intention can put the whole platoon in danger by reducing the safety [65]. Considering the primacy and manifold; responsibilities of PL, it is of utmost importance to elect the most suitable and trustworthy leader for the platoon.

In this section, six different use case scenarios are discussed where a vehicle platoon is required to make a cooperative decision to elect a leader for a group of vehicles to carry on the platooning.

• 1.

  Electing a leader while forming the platoon for the first time: In this scenario, while forming the platoon for the first time, a vehicle needs to be elected as a platoon leader to start the platooning. The platoon leader is then responsible for the coordination and all the operation in the platoon. Each vehicle wishing to drive in a platoon manually needs to admit it by selecting the platooning mode.

• 2.

  Electing a leader when two platoons merge: In this use case, two platoons of different length traveling on the same lane decides to merge and make one platoon aiming to make it more efficient to travel in the platoon. Electing a leader in the merging scenario is a challenging maneuver because of the interferences triggered by unintended vehicles that join the platoon. When two-platoon merge, it is required to re-elect a suitable leader to continue the platooning successfully. Finally, the merging platoon has to share its information with the elected leader of the platoon.

• 3.

  Electing a leader when a platoon splits: In the platoon splitting scenario, the platoon splits into two or more sub-platoons at a point while continuing to drive to their intended destinations. Once the platoon splitting request is acknowledged by the platoon leader, it is indispensable to elect a new leading vehicle to take in charge of leadership roles and responsibilities for the split platoon.

• 4.

  Electing a leader while adding a new vehicle: This scenario takes place when a new vehicle dynamically desires to become a part of the already formed platoon, driving on the road. Based on the characteristics of the new vehicle, it is recommended to take the consensus of all the vehicles and re-elect the new leader for the platoon since there is a possibility that the newly added vehicle is more suitable to lead the platoon. However, this use case could be inefficient as the vehicles have to undergo the entire process of leader election every time, which is a waste of time and resources.

• 5.

  Electing a leader when a platoon dissolves fortuitously: While traveling in a platoon, sometimes, due to bad situational conditions, technical issues in a leader, or momentary traffic conditions leads to platoon dissolve. At this stage, the platoon cluster is disbanded, making the complete platoon stop the platooning. In this scenario, it is essential to re-elect the leader aiming to maintain and reap the full benefits of platooning.

• 6.

  Electing a leader when a leader leave maneuver takes place: In this use case, the vehicle platoon is required to re-elect a leader when the leader leave maneuver takes place. The leader leave maneuver can occur in two different scenarios. The first one is when a platoon leader reaches its destination and makes the leader leave request, whereas, in the second case, the leader vehicle may be inoperable due to any technical failure. In both cases, the leading vehicle is incapable to perform the leader’s role and responsibilities; thus, it is mandatory to re-elect a leader to carry on the platooning.

3.2.2. Solution Approaches and Challenges

The considerable amount of literature can be found on different perspectives of platooning, such as platoon formation [66,67], fuel consumption [68,69], CO₂ emission reduction [70], communication, string stability [71], control and coordination strategies [72], and security and safety [73,74]. However, relatively less importance has been directed to study how to elect an experienced driver, most suitable and trustworthy candidate vehicle as a platoon leader. Therefore, a leader election problem in vehicle platooning is of great interest and needs significant research.

Vehicle platooning has been the focus of multiple stakeholders: academia and the transportation industry. In recent years, ample research has been done on the leader election problem in wireless ad hoc networks [75,76]. However, these mechanisms are not directly applicable for vehicle platooning owing to distinct objectives, mobility, and context parameters. Literature directs little consideration to the leader election problem in platooning.

In this section, we provide an exhaustive review analysis of the platoon leader election studies. Inspired by the various solution approaches implemented to elect the leader, we carefully choose a visualization approach, showing the scope of the relevant research articles in Figure 8. Besides, based on the methodologies, we classify the solutions into four broad categories. The algorithm-based studies are the studies where the authors developed different algorithms to elect the leader. In protocol-based approaches, either the authors implemented new protocols or they improved the previous one leading to elect suitable leaders, whereas, in simulation- and numerical-based approaches, authors proposed different simulations and mathematical models to elect trustworthy leaders. Finally, the studies which overlap with each other are highlighted in the region 1,2,3 and region 4.

In Automated High System (AHS), usually, a special heavy vehicle is nominated as a platoon leader which restricts the election of platoon leading vehicles autonomously. To overcome this limitation, the authors proposed the Autonomous Platoon Leader Selection (APLS) mechanism [77]. The proposed mechanism exploited the mobility information: speed and velocity of neighboring vehicles to select the appropriate stable platoon leader intending to reduce the fuel consumption and traveling time of the vehicle. Based on the obtained mobility information, a suitability value (W) is computed for each vehicle by taking the difference of its velocity from the average velocity of all neighboring vehicles. Besides, the position of the vehicle is also taken into consideration by taking the average position of all vehicles. Eventually, the vehicle with the highest (W) value is selected as a platoon leader. In 2017, the authors extended this work by simulating the mechanism in the NS-3 simulator [78]. The APLS mechanism is evaluated based on three performance metrics: platoon formation message overhead, platoon stability, and platoon size. Despite the successful results, some limitations are associated with this approach. While selecting the leader, only the velocity and position of the vehicle are taken into consideration that are not good enough parameters to select a leader. Besides, the characteristics of the driver and vehicle are also not considered.

The goal of platooning is to optimize road throughput by traveling in a small inter-vehicle distance. In current vehicular platooning, if the platoon cluster splits due to technical or traffic issues, the platoon is still capable to elect a new leader to continue driving in platooning. However, this process fails to maintain the goal of maximizing road throughput. To address this problem, the SAE J2735 protocol was improved, and a new data element, the candidate vehicle is added to elect the candidate leader [79]. While joining the platoon, a vehicle has the option to set its status as a candidate leader by setting up the status value to 1. In the case of several following vehicles, wishing to be a candidate leader, the proposed approach prioritizes the candidate leader based on the status information of the vehicle. This approach made it easy to elect a candidate leader without delay based on priority information eliminating the need of going through the complete process of selecting a leader without delay. The authors did not elucidate which status information of the vehicle is modeled and how they computed this value to elect a leader. In addition, the experimental setup and results are not discussed in detail. In another research study [80], it is stated that in traditional platooning, the platoon leader is fixed and the platoon cluster has to disband in case of any uncertain situation that falls short the goals of platooning. Moreover, it is argued that it is extremely challenging to determine whether a vehicle is well enough to take the leading role of the platoon. The authors proposed a solution to this problem by applying the consensus-based Raft algorithm to automatically elect the leader in emergencies. For each vehicle, a numerical relative value is computed based on the vehicle’s performance aiming to ascertain which vehicle is capable to take the lead. After calculating the relative value, the leader is elected through the Raft algorithm. However, this study did not highlight how the numerical value is calculated and which performance metrics are taken into consideration while voting for a vehicle.

It is more advantageous traveling as a following vehicle than the leading vehicle since it is accountable for several duties and consumes more fuel [81]. The equal distribution of profit from platooning between all vehicles is crucial to collaborate and form the platoon; otherwise, competing companies will not be agreed to form a heterogeneous platoon. To mitigate this selfish behavior of users and to make it profitable for vehicles to lead, an incentivization framework should be there. Ref. [82] worked on in question problem and proposed a protocol named Leadership Incentives for Platoon (LIPs) for dynamic and heterogeneous platoons. A comprehensive incentivization system based on blockchain technology is implemented to provide near-equal benefits to the platoon leader. Several parameters (fuel price, distance, average savings of followers, etc.) and service charges are modeled to determine how much money a vehicle should pay to a leader to travel as the following vehicle in the platoon. However, this approach neglected the leader election issue and did not consider the position of vehicles in the platoon to compute the accurate monetary cost. Similarly, in another study [83], an urban platoon-driving model is developed to allow vehicles to choose the best path-matching platoons. Additionally, a platoon head rotational selection mechanism and an incentivization framework based on blockchain technology are implemented to incentivize the leaders. A rule is formulated to select the most credible and trustworthy vehicle as a platoon leader by calculating the reputation score of vehicles. Each elected platoon leader is required to drive a certain distance and receive service charges from the following vehicles. Finally, to incentivize the leader, the fuel consumption and services charges are modeled. In the proposed study, it is not discussed how the cost of fuel consumption is calculated and whether the simulated platoon is homogeneous or heterogeneous. The downsides of References [82,83] is that both studies are the introduction of the complete transaction system that increases the cost, and the estimation of the cost of fuel consumption is a non-trivial problem [84]. In 2018, Reference [64] also worked on a similar problem to elect a leader based on an incentivization strategy. A round-based consensus 1-of-n selection algorithm is used to elect a leader. To incentivize the platoon leader, they added the third party, the insurance company, to provide the subsidy to the leading vehicle on its next insurance premium. This study is merely a proposed idea and suggested that vehicular characteristics and driver rating information should also be considered while selecting a leader.

A malevolent elected leader can put the complete platoon into danger; therefore, it is important to select an honest, trusted, and reputable vehicle to accomplish leading roles. Ref. [85] utilized the blockchain at the roadside unit (RSU) and the platoon level to elect a trustworthy leader. Based on the reputation score, each vehicle voted for other vehicles consequently, a vehicle with the highest votes elected as a leader. Ref. [86] implemented the reliable trust-based platoon service recommendation scheme (REPLACE), assisting users to not elect a malicious badly-behaved platoon leader. Besides, a reputation score system, based on the vehicle’s historical performance feedback, is designed for the platoon leader to distinguish between the well-behaved and badly behaved vehicles.

To sum up the analysis, we uncover the several limitations of the current research studies:

• Most of the studies did not model the characteristics of the driver and vehicle while electing a leader.

• Less attention has been directed on how to re-elect a leader when the cluster disbands in an emergency or at traffic signals.

• Fewer studies focused on electing a leader in heterogeneous platoons.

• It has not yet been studied whether being a leader is more beneficial in homogeneous or heterogeneous platooning.

• The voting criteria or parameters to elect a leader need to be researched in detail.

• Some of the studies implemented voting mechanisms to elect a leader; however, the comparison of these mechanisms is missing in the literature.

• Several studies did not consider the position of the vehicle while calculating the money for fuel consumption that a follower has to pay to the leader.

• More work needs to be done on the incentivization frameworks and equal distribution of the profit gained by the platooning.

Having analyzed the literature review on the platoon leader election, in this section, we highlight and classify some of the trivial and still open research challenges that need to be taken into consideration while selecting a leader. Both academia and industry are looking into knitting the enabling technologies to reap the full benefits of platooning. A few obvious research questions may be formulated in this regard, such as:

• 1.

  Homogeneous and Heterogeneous

  • Question 1: Can a standardized platform be developed to choose a leader in homogeneous platooning, where all the vehicles can meet the leadership criteria? Do we need to choose the leader wisely if the vehicles are of the same fleet operators?

  • Question 2: In the near future, platoons will be of the mixed string of heavy motor vehicle (truck) and light motor vehicle (car). Which vehicle should be elected as the leading vehicle of the platoon? How can an appropriate platoon leader election take place in the string of mixed vehicles, considering all the communication and contextual requirements?

  • Question 3: Can real-life platooning be implemented as multi-brand platooning? If so, can we develop a universal mechanism to elect a suitable platoon leader in multi-brand?

  • Question 4: Would the multi-brands get agree to cooperate and exchange their private and confidential information with each other to elect a leader on the fly?

• 2.

  Communication

  • Question 1: How can the string stability of the platoon be maintained when the number of trailing vehicles increases? Do we need to look for new wireless communication technologies to overcome this issue? Or can this challenge be addressed by nominating a virtual platoon leader aiming to provide seamless and reliable communication to a long platoon?

  • Question 2: Which characteristics and parameters should be taken into consideration to select an intermediate virtual leader for a long platoon?

• 3.

  Modeling Driver and Vehicle’s Characteristics

  • Question 1: How can we model and rate the characteristics of the driver (age, behavior, mental stability, driving experience, trips, platooning training, traffic violation, and accident record) while selecting an appropriate leader? Do we need to model the characteristics of the leading vehicle driver, as it is expected that, in the near future, vehicular platooning will completely be driver-less?

  • Question 2: How can we develop a system good enough to model the characteristics of the vehicle (fuel quantity, tire pressure, mileage, and insurance status) while electing the leader? Do these characteristics have an impact on the suitability of the leading vehicle election?

• 4.

  Trustworthiness and Equal Profit Distribution

  • Question 1: One malicious badly-behaved platoon leader can lead the complete platoon into danger. What are the crucial factors that should be taken into consideration to determine the trustworthiness of the platoon leader?

  • Question 2: To mitigate the effects of trailing vehicles acting selfishly and to promote the platooning widely, how can we ensure the fairness and equal distribution of the platooning benefits for the leader?