1.

INTRODUCTION

Intelligent vehicles are vehicles that are equipped with various advanced technologies with numerous sensors that enhance their safety, efficiency and convenience to achieve certain targets. These vehicles are capable of sensing the environment around them and adapting the driving behavior accordingly [1].

Additionally, intelligent vehicles are equipped with sophisticated hardware and software components, which enable itself to identify and respond according to the surroundings that they are staying. Intelligent vehicles includes the utilization of advanced sensors, computer vision, machine learning and artificial intelligence to detect obstacles [2], identify routes and analyze traffic patterns, respectively.

Initially, driver control behavior modeling was originally studied to study the handling stability of cars from the perspective of a human-vehicle closed-loop system. With Segel’s proposal of automotive lateral dynamics, many driver models have appeared, most of which belong to direction control models and relatively few speed control models.

In recent years, with the rapid development of intelligent cars, there are many control algorithms for automobile motion, including direction control algorithms, speed control algorithms, and comprehensive control algorithms for direction and speed. While most of these algorithms are not named after driver models, in essence they invariably describe some kind of control behavior of the driver over the movement of the car. Most of the early driver models mainly used for closed-loop evaluation and optimization of vehicle handling stability by controlling the direction of car driving to follow the established trajectory, called direction control models [3].

As for controlling the direction traditional model, according to the expected trajectory in front and the current motion state of the car, an optimal pre-aiming lateral acceleration is determined through a certain pre-aiming strategy, so that the lateral displacement deviation between the driving trajectory of the car after the pre-aiming time and the driver’s expected trajectory can be minimized. Then, the lateral dynamics characteristics of the car, as well as the response delay and action lag characteristics of the driver itself, are corrected, and an optimal steering wheel operation command is obtained to ensure that the actual lateral acceleration response of the car is consistent with the driver’s optimal pre-aiming lateral acceleration. By repeating the pre-aiming and correction process described above over time, the car can be controlled to follow the driver’s intended trajectory.

Intelligent vehicles can also be connected to the Internet of Things (IoT), which can allow them to send and receive data from other vehicles and infrastructure.

Through interactive information transmission, the vehicles can enhance the security level of moving, improved traffic flow and better route optimization [4]. Intelligent vehicles are becoming increasingly important as the world moves towards self-driving technology, and they have the potential to revolutionize the way we drive.

Intelligent direction selection is a technology used in intelligent vehicles that enables them to automatically select the safest and most efficient route to a destination. This technology utilizes advanced sensors, computer vision, machine learning, and artificial intelligence to analyze the environment around the vehicle and determine the best path [5].

It can also receive real-time traffic data and adjust the route accordingly. This technology helps vehicles to avoid traffic jams, reduce fuel consumption, and ensure a safe journey. Intelligent direction selection is an important component of self-driving technology [6], and then it has the potential to revolutionize the way we travel.

The vehicle direction selection algorithm is a computer-based system for selecting the most appropriate route for a vehicle based on data collected from the environment [7]. It uses deep learning techniques to recognize patterns in the data and make decisions about which route is most appropriate.

The algorithm is trained on a variety of data sources such as road conditions, traffic signs, and other vehicles to ensure accuracy and optimal performance. The algorithm can be adjusted as needed to ensure the best possible route is selected. This algorithm has the potential to improve safety and efficiency of vehicle navigation [8].

Deep learning is a subset of artificial intelligence that uses multi-layered artificial neural networks to process data and deliver insights [9]. It is an advanced form of machine learning that is capable of learning complex patterns and making decisions without being explicitly programmed.

Deep learning is used in many applications, including image recognition, natural language processing and autonomous driving [10]. It has the potential to revolutionize the way we interact with technology and has already had a major impact in the fields of computer vision, robotics, and autonomous vehicles. Deep learning is an exciting and rapidly-evolving field that has the potential to change the world for the better [11].

In this paper, we propose a novel direction selection algorithms through utilizing deep learning, which is a type of artificial intelligence used in intelligent vehicles to enable vehicles to automatically select the safest and most efficient route to a certain destination.

The propose algorithm utilizes deep learning techniques to analyze the environment surrounding environment through inputted images feature from the vehicle sensors and determine the best path.

The propose algorithm can also receive real-time traffic data and adjust the route accordingly. Proposed technology can assist vehicles to avoid traffic jams, reduce fuel consumption and ensure a safe moving routes. The proposed method is an important component of self-driving technology and they have the potential to revolutionize the way we travel.

The rest of this paper will be organized as following descriptions:

Initially, a primary introduction about primary parameters and corresponding explanation is presenting in section 2. The system framework is demonstrating in section 3. Subsequently, section 4 will include the experimental results and comparison evaluations. Finally, section 5 will provide a conclusion about the proposed method and supply the future improvements.

2.

PRIMARY PARAMETERS AND EXPLANATIONS

In this section, we demonstrate the model primary parameter symbols and its corresponding explanations in following Table 1.

TABLE 1.

PRIMARY SYMBOLS AND EXPLANATIONS

3.

SYSTEM FRAMEWORK AND PROCEDURES OF PROPOSED MODEL

In this section, we initially introduce the main sequences of proposed model and explain the detail function of procedures. Subsequently, the system general framework diagram is given to show the system components.

3.2

System Framework

The deep learning framework is consisted of four primary components. Initially, convolutional layer, which is responsible for extracting features from the input data and classifying the data into different categories.

Subsequently, recurrent is responsible for processing the data over time and recognizing patterns in the data. Additionally, reinforcement is responsible for learning from experience and adjusting the parameters of the algorithm as needed to improve its performance.

Finally, decision making module is responsible for making decisions based on the patterns identified by the algorithm. It is responsible for selecting the most appropriate route based on the identified patterns.

Following Figure 1 demonstrates the framework of proposed model.

Fig. 1.

Framework of proposed model.

4.

EXPERIMENTAL RESULTS AND EVALUATIONS

In this section, we initially introduce the simulation environments and compared algorithms. Subsequently, The experimental results and corresponding analysis for proposed model are shown.

4.2

Experimental Results and Analysis

Following Figure 2 demonstrates the comparison results about selection accuracy calculates as number of correct selection divided by the total number of vehicles and the indicator will continuously increase number of vehicles.

Fig. 2.

Selection accuracy comparison results.

Another essential evaluation indicator is computation costs for system and we simulate the proposed model with other compared algorithm with same simulation environment. Following Figure 3 demonstrates the system computation costs results.

Fig. 3.

Selection accuracy comparison results.

From our extensive experimental results, we can conclude that our proposed model can successfully achieve the direction selection tasks for intelligent vehicles and the selection accuracy is much more higher than traditional dynamic programming algorithms. Additionally, the system computational costs is reasonable and acceptable for intelligent vehicles through comparing with other directions selection algorithms.

The use of deep learning has become increasingly popular in recent years. Deep learning is a type of artificial intelligence that uses neural networks to learn from data and make decisions. This type of algorithm is particularly useful for autonomous vehicles, as it can be used to make decisions about the best route to take based on the current environment. The deep learning algorithm works by taking in data from the environment, such as the current road conditions, traffic patterns, and other factors.

This data is then used to create a model of the environment, which is then used to make decisions about the best route to take. The algorithm is able to learn from its mistakes and adjust its decisions accordingly. One of the advantages of using deep learning for direction selection is that it can be used to make decisions in real-time. This means that the algorithm can quickly adjust its decisions based on the current environment, allowing the vehicle to make decisions quickly and accurately. Another advantage of using deep learning for direction selection is that it can be used to make decisions based on more complex data.

Overall, deep learning algorithms are becoming increasingly popular for direction selection in intelligent vehicles. This type of algorithm is able to make decisions quickly and accurately, and can take into account more complex data than traditional algorithms. This makes it an ideal choice for autonomous vehicles, as it can assist vehicles make decisions that are both efficient and safe.

5.

CONCLUSIONS

In conclusion, deep learning algorithms are becoming increasingly popular for direction selection in intelligent vehicles. This type of algorithm is able to make decisions quickly and accurately, and can take into account more complex data than traditional algorithms. This makes it an ideal choice for autonomous vehicles, as it can help them make decisions that are both efficient and safe. Deep learning algorithms are also able to learn from their mistakes and adjust their decisions accordingly, allowing them to make more accurate decisions in real-time. As such, deep learning algorithms are becoming an increasingly important tool for autonomous vehicles, and will likely continue to be used in the future. As for the future improvements, there are a few areas where deep learning algorithms for direction selection in intelligent vehicles can be improved. For example, the algorithms can be further optimized to make more accurate decisions in a shorter amount of time. Additionally, the algorithms can be improved to take into account more complex data, such as the current weather conditions, traffic patterns, and other factors. Finally, the algorithms can be improved to better handle edge cases, such as unexpected obstacles or changes in the environment. With these improvements, deep learning algorithms for direction selection in intelligent vehicles will become even more effective and reliable.