The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the states of electrified vehicle batteries. Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power capabilities. This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks. Comparisons between methods are shown in terms of data quality, inputs and outputs, test conditions, battery types, and stated accuracy to give readers a bigger picture view of the ML landscape for SOC and SOH estimation. Additionally, to provide insight into how to best approach with the comparison of different neural network structures, an FNN and long short-term memory (LSTM) RNN are trained fifty times each for 3000 epochs. The error is somewhat different for each training repetition due to the random initial values of the trainable parameters, demonstrating that it is important to train networks multiple times to achieve the best result. Furthermore, it is recommended that when performing a comparison among estimation techniques such as those presented in this review paper, the compared networks should have a similar number of learnable parameters and be trained and tested with identical data. Otherwise, it is difficult to make a general conclusion regarding the quality of a given estimation technique.

Accurate vehicle counting at intersections is crucial for assessing traffic flow and gaining insights into vehicle trajectories captured by traffic cameras. This paper introduces an innovative framework that leverages a fisheye camera system to count vehicle movements at intersections with two significant contributions: First, the proposed algorithm employs a novel zone-based counting methodology to categorize and collect trajectory data and autonomously learn movement patterns with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm at intersections. Second, as the algorithm becomes proficient in recognizing the paths traversed by vehicles, it seamlessly transitions into a hybrid mode, integrating both zone-based and path-based counting techniques. It enables accurate vehicle counting even in challenging scenarios involving broken tracks or partial trajectories. The performance of the proposed method is accessed by conducting experiments on three real-world fisheye camera footage datasets. The results demonstrate the efficacy of our novel approach, achieving an impressive F1 score exceeding 98% across all tested intersections, underscoring its potential for real-world applications in traffic management.

Roadside sensors offer a fixed, unobstructed vantage point that can overcome line-of-sight limitations in autonomous driving environments by sharing critical perception data with nearby road agents. While this cooperative approach enhances situational awareness, it also introduces significant computational and communication overhead for autonomous vehicles (AVs). To address this challenge, we propose the Heterogeneous Decision-Aware Attention Graph Transformer (HDAAGT)—a non-autoregressive, encoder-only transformer architecture designed for real-time vehicle trajectory prediction. HDAAGT processes detection data from roadside infrastructure to forecast future vehicle trajectories and communicates these predictions to surrounding agents. By offloading intensive computations from AVs and minimizing transmission latency, our approach improves responsiveness and enables more efficient cooperative perception at intersections and other complex driving scenarios. HDAAGT integrates lane positioning, traffic light states, and vehicle kinematics, enabling a decision-aware graph attention mechanism that models agent-agent and agent-environment interactions. By leveraging a fisheye-based detection and tracking pipeline, our approach eliminates the need for multiple cameras and enables HDAAGT to generate reliable trajectory predictions across the full intersection. We validate our model on the Fisheye-MARC and SinD datasets, demonstrating the capability of HDAAGT in predicting vehicle motion in complex urban intersections with a 1.28 m final displacement error. Additionally, we introduce a new 31k-frame fisheye intersection dataset, the largest of its kind in object tracking, to advance research in intersection-based trajectory prediction.