Optimized Controller Scheme for Autonomous Navigation in Infotainment on Internet-of-Vehicles

Question

1. What is the difference between VANET and IoT-based VANET?

2. What is the unique caching system for vehicular communication?

3. What are the limiting factors in existing vehicular communication systems?

4. What are the notable contributory models for traffic signal control systems?

Accepted Answer

VANET represents vehicular nodes as mobile nodes with wireless connectivity, while IoT-based VANET represents vehicular nodes as smart nodes capable of extensive networking, storage, and computation. VANET focuses on robust network infrastructure, while IoT-based VANET offers complete stack operation. IoT-based VANET is more likely to support extensive traffic relation operations in the future. The concept of VANET is classified as rural and urban, whereas proposed research work is focused on IoT-based VANET system. Table 1 shows the conventional research in VANET, while IoT-based VANET research work is more likely to support extensive traffic relation operations in the future. The evolution of IoT-based VANET will facilitate challenging traffic management and offer discrete dissemination of information computed cost-effectively. The proposed scheme integrates VANET with a collaborative cloud environment for faster access to traffic information and designs traffic signal systems to compute the best route using probability based on traffic density. A fuzzy-based controller is proposed for identifying the best path and time for vehicles at intersections, ensuring the least waiting time and less computational burden. The proposed scheme builds an intelligence of traffic information using a computational method.

Accepted Answer

The unique caching system for vehicular communication, discussed by Wu et al., utilizes an infotainment system to select a cache node for transmission content relaying services. This system aims to offer energy efficiency for vehicular communication systems. The study explores the selection of cache nodes and their role in improving communication efficiency. By leveraging the infotainment system, the caching system optimizes the transmission of content, enhancing the overall performance of vehicular communication networks.

Accepted Answer

The limiting factors in existing vehicular communication systems include a dominance of machine learning-based concepts without considering constraint-based perspectives, lack of time-based prioritization for vehicular nodes, minimal emphasis on topology construction for scalable performance, uniform traffic signal decisions without catering to specific vehicle demands, and limited focus on traffic density and wait time reduction. Additionally, existing studies mainly emphasize map-based navigational systems without incorporating smart/intelligent systems in infotainment. These factors hinder the development of efficient and adaptive vehicular communication systems.

Accepted Answer

Notable contributory models for traffic signal control systems include Ahn and Choi's system that focuses on reducing vehicle queue length and Gomez et al.'s study using a learning-based algorithm with reinforcement learning and computer vision. Ahn and Choi's model emphasizes communication system improvement and queue length reduction but lacks scalability due to the absence of collaborative networks. Gomez et al.'s study utilizes computer vision to reduce queue length at intersections, but it can be replaced with conventional sensory-based roadside units for better coverage. Both studies aim to reduce wait time and understand vehicular navigation demands, contributing to the motivation for the proposed design process.

Accepted Answer

The proposed system adopts graph theory to construct a topology of the VANET cloud system, considering the essential elements of the position of vehicular nodes, their respective direction, and speed. This approach allows for efficient communication and coordination among vehicular nodes and roadside units, enabling faster clearance at intersections and optimized route selection based on traffic density and infotainment system input. The graph theory-based topology facilitates the synchronization of cloud-based collaborative modules and smart traffic light systems, enhancing the overall performance and efficiency of the urban VANET system.

Accepted Answer

FCM for Road Selection is a module that considers topology control for VANET systems, exploring effective positions and routes using an objective function. It finalizes route decisions and disseminates them to vehicles. The design framework intersects four roadways, using fuzzy logic to control waiting and clearance times at intersections. The module monitors and tracks vehicular nodes with clearance signals, assessing traffic density and computing faster clearance routes. Algorithm-1 outputs the selected road. The system uses probability-based computation and a fuzzy ruleset to determine time clock durations. It offers distributed operation without affecting scalability, ensuring dynamic traffic load management. The module's ruleset doesn't burden memory, and information is lightweight for infotainment systems. The modules work together to achieve the study goal.

Accepted Answer

The Ruleset (RS) formulates several Rules (Rn) based on the conditions of the VTD. The Rn is computed using expression (1), where the variable represents the number of conditions and the number of roads. In this case, with 3 conditions and 4 roads, the value of Rn is 81. The conditions R {RW1, RW2, RW3, RW4} have similar conditions of VTD, either belonging to {(LTD), (MTD), (HTD)}. In all three conditions, the output of the probability of road section (Pr) will be a null set. In the rest of the combinations, the probability of road selected Pr will be any of the elements of R {RW1, RW2, RW3, RW4}, depending on the weight of the randomness of the VTD.

Accepted Answer

The CT variable in Eq. (2) represents a probability towards clock time of clearance by considering two dependable variables, e and d. The computation of primary variable e is carried out by the product of time t and summation of nc concerning Sr and incoming vehicular node nin.i.e., = ( ( ) + ) x. This computed value of e attribute respects the favorable duration associated with the clearance of all the incoming vehicular nodes for all the considered lanes towards the intersection point. Similarly, the variable d represents all the outgoing vehicular nodes. The empirical expression of CT in Eq. (2) represents a probability toward clock time of clearance, as it takes into account the time clock related to the exact duration of green, red, or yellow light, as well as the density of nodes at the intersection point. The challenge lies in maintaining the time clock in a way that justifies the clearance of vehicles from other lane systems while offering navigational information in the infotainment system.

Accepted Answer

The proposed scheme handles traffic density by performing live computation based on the specific road's traffic density. It computes towards the faster clearance of each vehicle from their intersection points. The scheme does not rely on map-based methods or iterative methodologies with dataset dependency. Instead, it uses fuzzy rules to make decisions based on the actual scenario of node density of traffic associated with all lanes connecting to the intersection point under observation. This approach allows for efficient and dynamic traffic management, ensuring optimal clearance and minimizing congestion at intersections.

Accepted Answer

Figure 5 demonstrates that the proposed system's consistency factor is high for all four intersection points across multiple iterations. As the number of iterations increases, more vehicles are assigned, leading to an increase in sensed information by roadside units. The figure highlights a bottleneck condition nearing the 50th iteration and approaching the 150th iteration at the 4th junction. However, the bottleneck duration is less than 1.976 minutes. The bottleneck occurs when traffic density exceeds the capacity of each road (50 vehicles). This outcome indicates that the proposed scheme can maintain consistency in disseminating navigational information under fluctuating traffic conditions. Although the initial decision-making time is a few minutes in the first iteration, Figure 6 shows that the clearance signal duration varies by approximately 1 minute for all intersection points. This result indicates less fluctuation in clearance signal arrival in the infotainment system, regardless of traffic density. It's important to note that there is no uniform clearance duration for all four roads simultaneously due to dynamic conditions.

Optimized Controller Scheme for Autonomous Navigation in Infotainment on Internet-of-Vehicles

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Most frequently asked questions

1. What is the difference between VANET and IoT-based VANET?

2. What is the unique caching system for vehicular communication?

3. What are the limiting factors in existing vehicular communication systems?

4. What are the notable contributory models for traffic signal control systems?

5. How does the proposed system utilize graph theory in VANET cloud system design?

6. FCM for Road Selection?

7. How does the RS formulate Rules based on VTD conditions?

8. How does CT variable in Eq. (2) represent a probability towards clock time of clearance?

9. How does the proposed scheme handle traffic density?

10. How does the proposed system's consistency factor perform at specific intersection points?

Citations

Optimized Controller Scheme for Autonomous Navigation in Infotainment on Internet-of-Vehicles

Novel Intelligent BSM Falsification Attack Detection System Using Trusted Neighbor Vehicle Approach in IoV

References

A Comprehensive Survey: Benefits, Services, Recent Works, Challenges, Security, and Use Cases for SDN-VANET

Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications.

Artificial Intelligence for Edge Service Optimization in Internet of Vehicles: A Survey

A Machine-Learning-Based Data-Centric Misbehavior Detection Model for Internet of Vehicles

A Lightweight Mutual Authentication Protocol for V2V Communication in Internet of Vehicles

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