Driving Maneuver Detection at Intersections for Connected Vehicles: A Micro-Cluster-Based Online Adaptable Approach

Real-time detection of oncoming vehicle maneuvers at intersections is essential for connected autonomous vehicles (CAVs) to plan safe paths and driving strategies. Most existing methods use supervised learning methods to construct behavior detection models and assume that most data have labels. Real data collected by the onboard sensor as a data stream is unstable, and there are outliers, concept drift, and evolution problems, potentially decreasing the detection accuracy. To this end, we propose a micro-cluster-based online adaptable (MCOA) approach. The framework consists of four parts: initial model construction, new class detection, classification using k-nearest neighbor (k-NN), and online update. First, k-means clustering is performed on the maneuvering behavior data, and cluster features are derived to obtain a set of micro-clusters (MCs) to establish the initial model. Second, we analyze the instances stored in the data block to detect new classes and use the k-NN to classify the incoming instances. Finally, the model is updated online using an update strategy based on error-driven representative learning, a time-effect function, and a local decision boundary. A driving simulator is used to collect experimental data consisting of left turns (LT), right turns (RT), and going straight (GS) to establish and evaluate the model. The results show that the proposed model achieves higher detection accuracy for early-stage intersection maneuvers and has stronger adaptability to new classes than benchmark algorithms.