Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles
Abstract
:1. Introduction
2. Analysis of Lane-Changing Behavior
3. Similarity Analysis of System
4. Construction of Lane-Changing Decision-Making Behavior Model Based on Molecular Interaction Potential
- The automation level of the Connected and Automated Vehicles is high, and the vehicle can complete the driving operation by itself;
- The Connected and Automated Vehicles can obtain the position and speed of themselves and surrounding vehicles in real time;
- The Connected and Automated Vehicles are unified standard cars and can communicate with each other.
4.1. Generation of Lane-Changing Intention
4.2. Explanation of Molecular Interaction Potential
4.3. Establishment of Molecular Interaction Potential Lane-Changing Model
5. Experimental Verification and Discussion
5.1. Platform and Environment of Simulation
5.2. Analysis of Lane-Changing Data
- (1)
- Figure 15a shows the variation of lateral speed for the lane-changing vehicle. At first, when the vehicle does not change lanes, its lateral speed is zero, and the vehicle keeps driving along the center of the current lane. In the process of lane-changing, the lateral speed of the vehicle begins to change, and the change is to accelerate first and then decelerate. After arriving at the center of the target lane, the lateral speed of the lane-changing vehicle returns to zero. Finally, the vehicle drives in the target lane. It can be seen from the figure that the lane-changing vehicle only changes the lane once.
- (2)
- Figure 15b shows the variation of speed for the lane-changing vehicle. Sometimes the lane-changing vehicle accelerates and sometimes decelerates. Because the vehicle runs on the high-speed road with relatively few obstacles, its speed fluctuation range is relatively small, and it can finally run in a more stable state.
- (3)
- Figure 15c shows the variation of acceleration for the lane-changing vehicle. During the process of lane-changing, the acceleration of the vehicle fluctuates up and down near zero. It can be seen from the figure that the variation range of acceleration is −0.7 m/s2~0.7 m/s2, which enables the vehicle to drive stably with small speed fluctuation. Moreover, the information shown in Figure 15b,c can be mutually verified.
- (4)
- Figure 15d shows the variation of the offset of the right side of the lane-changing vehicle relative to the right side of the road. This offset actually represents the lateral position of the vehicle and can directly reflect the lane-changing information. When the vehicle drives in the current lane, the offset does not change. When the vehicle changes the lane, the offset will change, and the final change is the width of the single lane. It can be seen from the figure that the lane-changing vehicle only changes the lane once. In addition, the information shown in Figure 15a,d can be mutually verified.
5.3. Evaluation of Molecular Interaction Potential Lane-Changing Model
6. Conclusions
- Connected and Automated Vehicles have the characteristics of interactivity and dynamics. By analyzing its similarity with molecules, the molecular interaction potential theory is applied to the lane-changing scene, and the molecular interaction potential lane-changing model is established, which scientifically shows the lane-changing characteristics of the Connected and Automated Vehicles.
- The molecular interaction potential lane-changing model unifies the attraction and repulsion into a whole while considering the dynamic influencing factors, so as to form the reasonable lane-changing decision-making mechanism, so that the Connected and Automated Vehicles can implement lane-changing safely and efficiently. The experimental results show that, compared with the SL2015 lane-changing model, the average speed under the molecular interaction potential lane-changing model is increased by 3.26%, the speed fluctuation is reduced by 15.5%, and the number of passed vehicles is increased by 3.26%. In addition, there is no collision accident and the perturbation of lane-changing vehicle to the traffic flow of the target lane is small. Therefore, the molecular interaction potential lane-changing model has good safety, stability, and road utilization.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Value |
---|---|
α | 0.0546 |
β | 0.0514 |
μ | 0.0056 |
Additional Parameters | Range |
---|---|
LcSublane | [0, inf] |
LcPushy | [0, 1] |
LcAssertive | [0, 1] |
LcImpatience | [−1, 1] |
Parameters | SL2015 Lane-Changing Model | Molecular Interaction Potential Lane-Changing Model |
---|---|---|
Vehicle length (m) | 4.8 | 4.8 |
Vehicle width (m) | 1.8 | 1.8 |
Vehicle color | blue | red |
Maximum speed (m/s) | 33.33 | 33.33 |
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Zhang, K.; Qu, D.; Song, H.; Wang, T.; Dai, S. Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles. Sustainability 2022, 14, 11049. https://doi.org/10.3390/su141711049
Zhang K, Qu D, Song H, Wang T, Dai S. Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles. Sustainability. 2022; 14(17):11049. https://doi.org/10.3390/su141711049
Chicago/Turabian StyleZhang, Kekun, Dayi Qu, Hui Song, Tao Wang, and Shouchen Dai. 2022. "Analysis of Lane-Changing Decision-Making Behavior and Molecular Interaction Potential Modeling for Connected and Automated Vehicles" Sustainability 14, no. 17: 11049. https://doi.org/10.3390/su141711049