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Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM. [ABSTRACT FROM AUTHOR]

Accurate predictions of future pedestrian trajectory could prevent a considerable number of traffic injuries and improve pedestrian safety. It involves multiple sources of information and real-time interactions, e.g., vehicle speed and ego-motion, pedestrian intention and historical locations. Existing methods directly apply a simple concatenation operation to combine multiple cues while their dynamics over time are less studied. In this paper, we propose a novel Long Short-Term Memory (LSTM), namely, to incorporate multiple sources of information from pedestrians and vehicles adaptively. Different from LSTM, our considers mutual interactions and explores intrinsic relations among multiple cues. First, we introduce extra memory cells to improve the transferability of LSTMs in modeling future variations. These extra memory cells include a speed cell to explicitly model vehicle speed dynamics, an intention cell to dynamically analyze pedestrian crossing intentions and a correlation cell to exploit correlations among temporal frames. These three individual cells uncover the future movement of vehicles, pedestrians and global scenes. Second, we propose a gated shifting operation to learn the movement of pedestrians. The intention of crossing the road or not would significantly affect pedestrian’s spatial locations. To this end, global scene dynamics and pedestrian intention information are leveraged to model the spatial shifts. Third, we integrate the speed variations to the output gate and dynamically reweight the output channels via the scaling of vehicle speed. The movement of the vehicle would alter the scale of the predicted pedestrian bounding box: as the vehicle gets closer to the pedestrian, the bounding box is enlarging. Our rescaling process captures the relative movement and updates the size of pedestrian bounding boxes accordingly. Experiments conducted on three pedestrian trajectory forecasting benchmarks show that our achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) is a paradigm of ITS that incorporates automobiles, transportation, information sharing, and traffic infrastructure management with the aim to improve safety on roads. Federated learning enables the Internet of Vehicles (IoV) networks with minimal data communication overhead and privacy infringement. The vehicles participate in model training for improving the model accuracy at the global level and enhancing road safety to a great extent. In this paper, we apply federated learning to driver behavior analysis in which the vehicles in the network collaborate to train CNN-LSTM and CNN-Bi-LSTM deep learning models for driver behavior classification (safe, unsafe, or fatigue) without sharing raw data. The CNN-LSTM and CNN-Bi-LSTM models have been implemented and analyzed using real-time driver's behavior data, collected using inbuilt smartphone sensors or vehicle onboard devices. The proposed model has been evaluated using the performance parameters viz., accuracy, Area Under Curve (AUC), loss, precision, and recall. Both IID and non-IID types of datasets have been considered for analyzing the diversity of the proposed model. It is evident that implementation of federated learning architecture provided similar parametric outcomes on IID and non-IID datasets in comparison to contemporary deep learning implementations on a non-distributed dataset. The proposed work aims towards designing a reliable intelligent transportation system using federated learning. [ABSTRACT FROM AUTHOR]

• Proposed a federated learning framework to develop personalized driving assistance models. • Trained forward collision warning (FCW) models through collecting updated model parameters rather than raw data. • Introduced a driver-specific batch normalization (BN) layer to allow models adapting to heterogeneous driving behaviors. • Developed Fed-BN-LSTM models with the average testing accuracy of 84.88%. Current designs of advanced driving assistance systems (ADAS) mainly developed uniform collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead to low drivers' trust in. To address this issue, developing personalized driving assistance algorithms is a promising approach. However, current personalization systems were mainly implemented through manually adjusting warning trigger thresholds, which would be less feasible for overall drivers as certain domain expertise is required to set personal thresholds accurately. Other personalization techniques exploited individual drivers' data to build personalized models. Such approach could learn personal behavior but requires impractical large-scale individual data collections. To fill up the gaps, self-adaptive algorithms for personalized forward collision warning (FCW) based on federated learning were proposed in this study. A baseline model was developed by long short-term memory (LSTM) for FCW. Federated learning framework was then introduced to collect knowledge from multiple drivers with privacy preserving. Specifically, a general cloud server model was trained by collecting updated parameters from individual vehicle server models rather than collecting raw data. Besides, a driver-specific batch normalization (BN) layer was added into each vehicle server model to address the heterogeneity of driving behaviors. Experiments show empirically that the proposed federated-based personalized models with the BN layer showed to have the best performance. The average modeling accuracy has reached 84.88% and the performance is comparable to conventional total data collection training approach, where the additional BN layer could increase the accuracy by 3.48%. Finally, applications of the proposed framework and its further investigations have been discussed. [ABSTRACT FROM AUTHOR]