A novel density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy

Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for different kinds of clustering tasks. However, deciding the cutoff distance $${d}_{c}$$ d c largely depends on human experience which greatly affects clustering results. In addition, the selection of cluster centers requires manual participation which affects the efficiency of the algorithm. In order to solve these problems, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (KNN-ADPC). A clusters merging strategy is proposed to automatically aggregate over-segmented clusters. Additionally, the K nearest neighbors are adopted to divide data points more reasonably. There is only one parameter in KNN-ADPC algorithm, and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC, and DPC-KNN.

A Collaborative Merging Strategy with Lane Changing in Multilane Freeway On-Ramp Area with V2X Network

The merging area of the freeway is an area with a high incidence of traffic accidents. With the development of connected and automated vehicles (CAVs) and V2X technology, the traffic efficiency of freeway ramp areas has been significantly improved. However, current research mostly focuses on merging a single mainline lane and ramp, and there are few cases of multiple lanes. In this paper, we present a collaborative merging model with a rule-based lane-changing strategy in a V2X environment. First, the vehicle selects the appropriate gap to change lanes safely without affecting other vehicles. Meanwhile, we established a linear time discrete model to optimize the trajectory of vehicles in real-time. Finally, the proposed model and strategy were implemented in SUMO and Python. The simulation results showed that the merging model we proposed based on the lane-changing strategy had good performance in terms of the number of stops, average delay, and average speed.

Lane-Merging Strategy for a Self-Driving Car in Dense Traffic Using the Stackelberg Game Approach

This paper presents the lane-merging strategy for self-driving cars in dense traffic using the Stackelberg game approach. From the perspective of the self-driving car, in order to make sufficient space to merge into the next lane, a self-driving car should interact with the vehicles in the next lane. In heavy traffic, where the possible actions of the vehicle are pretty limited, it is possible to conjecture the driving intentions of the vehicles from their behaviors. For example, by observing the speed changes of the human-driver in the next lane, the self-driving car can estimate its driving intention in real time, much in the same way of a human driver. We use the principle of Stackelberg competition to make the optimal decision for the self-driving car based on the predicted reaction of the interacting vehicles in the next lane. In this way, according to the traffic circumstances, a self-driving car can decide whether to merge or not. In addition, by limiting the number of interacting vehicles, the computational burden is manageable enough to be implemented in production vehicles. We verify the efficiency of the proposed method through the case studies for different test scenarios, and the test results show that our approach is closer to the human-like decision-making strategy, as compared to the conventional rule-based method.