Assessment Possibilities for Trajectory Fusion of Two Vehicles in the Case of Automated Vehicles

Because of the increasing integration of automation and cutting-edge technology into modern transportation systems, the development of efficient and dependable Automated Vehicles (AVs) has become an important area of research. One of the most important characteristics of AVs is their ability to perceive and navigate complex environments safely. The process of combining trajectory data from multiple vehicles, known as trajectory fusion, has the potential to improve AV perception. The feasibility of implementing trajectory fusion in the context of two autonomous vehicles coexisting in the same operational environment is investigated in this study. The primary goal of this research is to investigate the effectiveness of trajectory fusion in increasing the environmental awareness of Automated Vehicles (AVs), while also investigating its implications for the decision-making mechanisms that govern these vehicles. The MATLAB Simulink simulation platform was essential in carrying out this investigation. The intricate dynamics of trajectory fusion within the context of dual AVs were meticulously analyzed and evaluated using this software.


Introduction
The evolution of automated vehicles (AVs) has transformed transportation networks, promising increased safety, efficiency, and convenience.However, the success of AVs deployment is dependent on their capacity to effectively perceive and navigate complicated situations.The idea of trajectory fusion, which combines the trajectory data from many vehicles to produce a more complete depiction of the environment, is essential to obtain this capability.This paper delves into the assessment possibilities of trajectory fusion within the context of two automated vehicles, employing MATLAB Simulink simulations to explore its implications and advantages.In order to study the consequences and benefits of trajectory fusion, this research uses MATLAB Simulink simulations to examine the assessment possibilities of the technique in the setting of two automated vehicles.

State of the art
A potential answer to the problems brought on by separate vehicle sensors is trajectory fusion.It can lessen sensor limits, enhance perceptual precision, and make it possible to have a more comprehensive grasp of the world.Trajectory fusion offers the potential to improve AVs' situational awareness and decision-making abilities by incorporating data from nearby vehicles [1].
Cooperative perception is a concept that is introduced through AV cooperation through trajectory fusion.By utilizing one another's observations, AVs can efficiently increase the range of their sensing Simulation is a crucial stage in assessing trajectory fusion.A flexible environment for modeling and evaluating intricate interactions between AVs is offered by MATLAB Simulink.Simulations enable researchers to evaluate the performance of trajectory fusion algorithms under various circumstances by generating controlled and reproducible scenarios [3].
Simulations allow for a more in-depth examination of the impact of trajectory fusion on AV performance.Researchers can test the resilience and scalability of trajectory fusion algorithms by varying parameters such as sensor noise, communication range, and environmental complexity [4].This method allows for the investigation of many scenarios and provides insights into potential problems.
The paperwork [5] seeks to answer the following fundamental problems about trajectory fusion: (1) Explain how trajectory fusion improves AV vision and decision-making.(2) What are the difficulties in establishing trajectory fusion for two AVs?Some studies aim to provide empirical data and insights into the possible benefits and complexity of trajectory fusion using MATLAB Simulink simulations.
The subsequent sections will delve into the methodology, detailing the simulation setup, trajectory fusion algorithms, and assessment metrics employed.By focusing on a scenario involving two AVs, the study [6] strikes a balance between realism and controllability, enabling a comprehensive analysis of trajectory fusion's effects.
The findings of the study [7] have ramifications for the development of cooperative AV systems, contributing to a better understanding of the possibilities of trajectory fusion.As the AV landscape evolves, trajectory fusion could be critical in improving AV perception and decision-making capabilities in complex contexts.
The paper [8] addresses the challenges of autonomous driving in complex environments.The authors present unique trajectory planning algorithms that take into account real-world circumstances, including interactions with traffic dynamics and human drivers.The project aims to improve the adaptability and safety of highly autonomous vehicles by stressing maneuver-based methods.The research advances the science of autonomous driving by providing insights into successful trajectory planning approaches for navigating real roads with complex traffic circumstances and driver interactions through a thorough analysis.
The aim of this paper is to demonstrate the significance of trajectory fusion for enhancing the perceptual capabilities of AVs and paving the way for further advancements in AV technology.The simulation is used to demonstrate the practical development of a track-level fuser and the use of the object track data format.The driving scenario and models derived from the "Automated Driving Toolbox", which seamlessly interact with the tracking and track fusion models derived from the "Sensor Fusion and Tracking Toolbox", within the simulated environment using MATLAB Simulink.This dynamic fusion demonstrates the promise of sophisticated fusion approaches for improving the efficacy and performance of automated driving systems.

Materials and methods
In the analyzed scenario (Figure 1), two cars are driving down a road with constant speed of 12 m/s (approx.43 km/h).A lower speed than the locality's legal speed was considered.Vehicle 1 is positioned in front and is outfitted with two forward-facing sensors: a short-range radar (SRR) and a vision sensor.Long-range radar (LRR) is installed in Vehicle 2, which is trailing Vehicle 1 at a distance of 10 meters.Other cars are parked along the right side of the road.Notably, a pedestrian is standing in the space between two of the parked vehicles.The action takes place at the X-coordinate of 60 meters when we see a symbolic representation of the pedestrian's presence.Through the use of various sensor setups, inter-vehicle distances, and the presence of pedestrians, this scenario captures the complexity present in real-world surroundings.Such situations demonstrate the necessity of efficient sensor fusion and trajectory planning techniques.
Due to the close proximity of Vehicles 2 and 1 (10 m), Vehicle 2's radar sensor coverage is blocked by Vehicle 1.As a result, tracks transmitted from Vehicle 1 serve as the basic initialization for the majority of the tracks that the track fuser on Vehicle 2 maintains.
In Vehicle 1, a pair of sensors send data to separate local trackers.These trackers monitor objects using local sensor detections and then feed these confined trajectories to the Vehicle's track fusion system.Vehicle 2, on the other hand, is equipped with a single sensor that relays detections to its dedicated local tracker.The local track fusion algorithm within Vehicle 2 then uses the local trajectories solely from Vehicle 2 as input.A basic functional diagram of these sensors on vehicles can be seen in Figure 2. The fused tracks from one Vehicle are updated by the fused tracks on the other Vehicle in this architecture.The first vehicle is then transmitted these fused tracks.When updating the track fuser with tracks from another vehicle, the rumor propagation must be prevented.
Vehicle 2 transmits track information about the item to Vehicle 1, which is the only one really tracking it.The track fuser on Vehicle 1 must thus be aware that the tracks it receives about this item from Vehicle 2 do not, in fact, include any new information that has been updated by a different source.To distinguish between tracks that include new information and tracks that simply repeat information, Vehicle 2 must be specified as an external source to the track fuser on Vehicle 1, and Vehicle 1 as an external source to the track fuser on Vehicle 2. Only tracks that are modified by a track fuser based on information from an internal source must be defined as self-reported.As a result, the track fuser in each Vehicle can disregard updates from tracks that bounce back and forth between track fusers without containing any new information.

Results and interpretation
When the simulation first starts (Figure 3), the behavior takes the following course: Vehicle 1 begins by recognizing the presence of parked automobiles along the right side of the street.The linked tracks for these stationary Vehicles are then put through a verification process to confirm their existence and characteristics.In the initial phase, Vehicle 2's tracker dedicates its resources entirely to detecting and monitoring the immediate presence of Vehicle 1, which is positioned directly ahead.As this occurs, Vehicle 1's track fuser enters a confirmation process to ensure the accuracy of the identified tracks.Once the confirmation is successfully accomplished, these verified tracks are then shared, a process initiated by Vehicle 1's track fuser.They are subsequently absorbed and seamlessly integrated into the track fuser of Vehicle 2. This orchestrated synchronization within the realm of track fusion bestows upon Vehicle 2 an advanced level of comprehension concerning the existence and dynamic behavior of these tracks.
This interplay showcases the collaborative aspect of track fusion, where one vehicle's tracking information benefits another, enhancing overall situational awareness and object-tracking proficiency.The intricate synchronization within this fusion process ensures that the data shared between vehicles is not only accurate but also contributes to a more comprehensive understanding of the environment, enabling safer and more efficient automated vehicle operations.The scenario progresses when Vehicle 2 successfully detects and tracks the nearby parked cars, fusing their tracks with those of Vehicle 1.This shows the Vehicle's expanding capabilities.Vehicle 2 successfully locates and tracks the pedestrian at the 4-second mark, instantly fusing the corresponding track at the 4.4-second mark into the simulation (Figure 4).However, there is a further delay of about two seconds before the pedestrian is autonomously detected and tracked by Vehicle 2's internal sensors (Figure 4).This delay could have negative effects on pedestrian safety, especially if the pedestrian starts to cross during this time.This emphasizes the significance of quick detection and fusion to enable quick responses and improve safety in dynamic circumstances.Track fusion often offers advantages in terms of faster and more accurate object detection because it combines information from multiple sensors, providing a more comprehensive view of the surroundings.Comparing the scenario with fusion tracks and the one with no fusion track, it can help to evaluate the effectiveness of track fusion in improving detection time and overall safety in automated vehicles.
Finally, in the final phase of the simulation (Figure 5), it is critical to further explore how the vehicles behave when they pass the objects, causing them to leave their field of view.The fused tracks connected to these objects are in this case discarded by both trackers.This intentional track-dropping highlights that the exchanged fused tracks between the two Vehicles comply to a system that maintains accuracy and validity in the trajectory information instead of perpetuating rumors.This method confirms that the trajectory fusion system maintains information precision without unintentionally distributing speculative data amongst Vehicles and underlines the strict dedication to reliability.

Conclusions
The simulation emphasized the real-world advantages of track-to-track fusion and highlighted its potential to raise safety and situational awareness in the context of automotive applications.In the complex world of automotive applications, it effectively illustrated how this fusion method has the ability to drastically raise safety and increase situational awareness.The simulation demonstrated the crucial role that track-to-track fusion plays in the development of automated Vehicles by linking theory and application, and it offered a promising trajectory development towards safer and more perceptive driving situations.The results of the simulation thus confirm the importance of fusion techniques in promoting the integration of autonomous cars into real-world environments while enhancing their general operational efficacy.
The significance of trajectory fusion for improving the perceptual capacities of AVs is shown in this research, which opens the door for further developments in AV technology.The simulation clearly highlighted the concrete real-world advantages inherent in track-to-track fusion by utilizing the strong MATLAB Simulink framework.The knowledge gathered from this work can be used to build trajectory fusion techniques that can be incorporated into the next generation of automated cars, ultimately leading to more secure and dependable autonomous transportation systems.

Figure 2 .
Figure 2. Functional diagram of the vehicle's sensors.

Figure 3 .
Figure 3.The initial position of the Vehicle's.

Figure 4 .
Figure 4.The fusion phase of the sensors within the simulation.

Figure 5 .
Figure 5.The final phase of the simulation.
This collaborative strategy shows promise for overcoming difficulties like occlusions, sensor errors, and ambiguous situations [2]. 2capabilities.