Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach
Abstract
:1. Introduction
- Fuzzy logic-based lane-changing decision model: We proposes a novel lane-changing decision model based on fuzzy logic and use a quintic polynomial function to plan the lane-changing trajectory. This approach optimizes both driving comfort and efficiency, effectively addressing the complexities involved in various lane-changing scenarios.
- Trajectory optimization using Particle Swarm Optimization (PSO): An objective function is formulated that considers both the time required for lane-changing and the maximum acceleration, ensuring an optimal balance between these factors. The PSO algorithm is utilized for real-time trajectory optimization, leveraging Vehicle-to-Everything (V2X) communication capabilities for dynamic adjustments based on changing traffic conditions.
- Agile lane-changing decision model for urgent scenarios: An agile decision model is developed to handle urgent lane-changing situations. The proposed decision model is capable of rapidly re-planning trajectories and communicating critical data back to the central information processing hub. Comprehensive simulations using Carsim and Simulink validate the capabilities of the model, demonstrating significant improvements in stability and safety during the lane-changing process.
2. Related Works
2.1. Autonomous Lane Change Strategy Based on V2X
2.2. Trajectory Planning Methods
2.3. Network Security Protection Strategies
3. Network Architecture
4. Fuzzy Logic-Based Lane Change Decision Model
4.1. Lane Change Scenario Analysis
- The speed of the vehicle remains constant during lane changes.
- The lateral and longitudinal movements of the vehicle do not interfere with each other during lane changes.
- The vehicles in the lane-change scenario use the same specifications and models.
4.2. Polynomial-Based Trajectory Planning for Lane Changes
4.3. Lane Change Distance Model
4.4. Polynomial-Based Trajectory Planning
5. Trajectory Planning Using Quintic Polynomials
- Each individual in a group can be considered a particle; collectively, they form a particle swarm. Assuming a swarm of M particles exploring an N-dimensional space, each particle is assigned a ‘position’, as follows:
- In each particle, the position serves as a potential solution. Its fitness value is calculated by inserting it into the objective function, with its worth determined based on this fitness value. The optimal position of each particle is recorded during each search process:
- The best position among all particles is regarded as the optimal solution of the entire particle population during this search process:
- This process repeats until the global optimal solution is found. Each search necessitates the update of particle velocity and position. The velocity of the i-th particle is represented as follows:
- The velocities and positions of the particles are updated using the following equations:
6. Results and Discussions
6.1. Parameter Setting
6.2. Free Lane-Change Decision Model
- For the time interval , the relative speed value is low and the relative distance value is high, maintaining the lane change inclination of the IV at a high level.
- For the time interval , the relative speed increases and the relative distance decreases; consequently, the lane change inclination of the IV decreases.
- For the time interval , as the relative speed and relative distance both increase, the lane change inclination of the IV decreases further.
- For the time interval , with the relative speed low and the relative distance high, the inclination of the IV to change lanes remains at a high level.
6.3. Autonomous Lane Change Trajectory Planning Verification
6.4. Trajectory Tracking Verification
7. Discussion on Limitations and Future Enhancements
8. Conclusions
- The integration of fuzzy logic theory with real-time vehicle-to-vehicle communication allows for a robust lane change decision model. This model accurately reflects lane change intentions and enhances vehicle safety by dynamically adjusting to surrounding traffic conditions. The protective function is enhanced by reducing the likelihood of collisions through informed decision-making processes.
- The trajectory planning approach utilizing quintic polynomials and particle swarm optimization optimizes lane change timing and path smoothness, contributing to technical support for IV development by providing a framework for real-time trajectory adjustments based on dynamic traffic data. The proposed method’s application in practice can improve traffic flow and reduce congestion by enabling smoother and more predictable lane changes.
- While our simulation and testing scenarios were not exhaustive, they provide a foundation for future research. Expanding the range of scenarios to include variable speeds and mixed traffic conditions will further enhance the model’s applicability. Establishing specific thresholds and safety margins will be crucial for practical implementation, where IVs need to safely navigate complex real-world environments.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
IV | Intelligent Vehicle |
ITS | Intelligent Transportation System |
RNN | Recurrent Neural Network |
RF | Random Forest |
V2X | Vehicle-to-Everything |
DQN | Deep Q-Network |
IoV | Internet of Vehicles |
SVM | Support Vector Machine |
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Notation | Description | Value |
---|---|---|
C-Class | Model of this vehicle | AITO 9 |
Pavement adhesion coefficient | 0.9 | |
The Longitudinal distance between and | 35 m | |
The Longitudinal distance between and | 40 m | |
The initial distance between and | 50 m | |
The speed of the | 20 m/s | |
The speed of the | 18 m/s | |
The speed of the | 22 m/s | |
d | Lane width | 3.5 m |
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Share and Cite
He, C.; Jiang, W.; Li, J.; Wei, J.; Guo, J.; Zhang, Q. Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach. World Electr. Veh. J. 2024, 15, 403. https://doi.org/10.3390/wevj15090403
He C, Jiang W, Li J, Wei J, Guo J, Zhang Q. Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach. World Electric Vehicle Journal. 2024; 15(9):403. https://doi.org/10.3390/wevj15090403
Chicago/Turabian StyleHe, Chao, Wenhui Jiang, Junting Li, Jian Wei, Jiang Guo, and Qiankun Zhang. 2024. "Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach" World Electric Vehicle Journal 15, no. 9: 403. https://doi.org/10.3390/wevj15090403