Accurate vehicle trajectory prediction is critical for autonomous vehicles and advanced driver assistance systems to make driving decisions and improve traffic safety. This paper proposes a novel Temporal Multi-task Mixture Of Experts (TMMOE) model for simultaneously predicting the vehicle trajectory and driving intention, considering interconnections among tasks. As for the methodology, the proposed model consists of three layers: a shared layer, an expert layer, and a fully connected layer. In more detail, the first layer utilizes Temporal Convolutional Network (TCN) to extract temporal features whereas the expert layer incorporates the gating mechanism to memorize and filter the temporal dependence of sequences and, finally, the fully connected layer is applied to integrate and export prediction results. Furthermore, the homoscedastic uncertainty algorithm is used to construct the multi-task loss function. The open data source CitySim dataset is chosen to validate the performance of the proposed TMMOE model; moreover, a novel lane line reconstruction method is introduced to mitigate measurement errors of the dataset. Two-part information, including the history of the vehicle's trajectory and the interaction indicators, are employed as the input variables. The result indicates that the TMMOE algorithm exhibits superior performance when compared to the other five models, including Long Short-Term Memory (LSTM) Network, Convolutional Neural Network (CNN), CNN-LSTM, TCN, and Gate Recurrent Unit (GRU), while considering varying input sequence lengths are 3 s, 6 s, and 9 s respectively. Finally, the sensitivity analysis demonstrates that considering driving intentions in vehicle trajectory prediction can significantly enhance the prediction accuracy of vehicle trajectories.

More attention should be given to bicycle-motor vehicle (BMV) crashes, as cyclists are at a higher risk of suffering injuries than motor vehicle users in a crash. This study aims to explore the fac...

Aggressive driving is common across the world. While most aggressive driving is conscious, some aggressive driving behavior may be unconscious on part of motor vehicle drivers. Perceptual bias of aggressive driving behavior is one of the main causes of traffic accidents. This paper focuses on identifying impact factors related to aggressive driving perceptual bias. Questionnaire data from 690 drivers, collected from a drivers’ retraining course administered by the Traffic Management Bureau in Nanjing, China, were used to collect drivers’ socioeconomic characteristics, personality traits, and external environment data. Actual penalty points were considered as an objective indicator and Gaussian mixture model (GMM) was used to cluster an objective indicator into different levels. The driving anger expression (DAX) was used to measure drivers’ self-assessment of aggressive driving behavior and then to identify perceptual biases. Then a binary logistic model was estimated to explore the influence of different factors on drivers’ perceptual bias of aggressive driving behavior. Results showed that bus drivers were less likely to have perceptual bias of aggressive driving behavior. Truck drivers, drivers with an extraversion characteristic, and drivers who have dissatisfaction with road infrastructure and actual work were likely to have a perceptual bias. The findings are potentially beneficial for proposing targeted countermeasures to identify dangerous drivers and improve drivers’ safety awareness.