2,641 research outputs found
CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory
Active safety systems on vehicles often face problems with false alarms. Most
active safety systems predict the driver's trajectory with the assumption that
the driver is always in a normal emotion, and then infer risks. However, the
driver's trajectory uncertainty increases under abnormal emotions. This paper
proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle
trajectories under abnormal emotions. At the physical level, the interaction
features between vehicles are extracted by the physical GCN module. At the
cognitive level, SOR cognitive theory is used as prior knowledge to build a
Dynamic Bayesian Network (DBN) structure. The conditional probability and state
transition probability of nodes from the calibrated SOR-DBN quantify the causal
relationship between cognitive factors, which is embedded into the cognitive
GCN module to extract the characteristics of the influence mechanism of
emotions on driving behavior. The CARLA-SUMO joint driving simulation platform
was built to develop dangerous pre-crash scenarios. Methods of recreating
traffic scenes were used to naturally induce abnormal emotions. The experiment
collected data from 26 participants to verify the proposed model. Compared with
the model that only considers physical motion features, the prediction accuracy
of the proposed model is increased by 68.70%. Furthermore,considering the
SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with
other advanced trajectory prediction models, the results of CPSOR-GCN also have
lower errors. This model can be integrated into active safety systems to better
adapt to the driver's emotions, which could effectively reduce false alarms.Comment: 15 pages, 31 figures, submitted to IEEE Transactions on Intelligent
Vehicle
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
Accurate prediction of vehicle trajectories is vital for advanced driver
assistance systems and autonomous vehicles. Existing methods mainly rely on
generic trajectory predictions derived from large datasets, overlooking the
personalized driving patterns of individual drivers. To address this gap, we
propose an approach for interaction-aware personalized vehicle trajectory
prediction that incorporates temporal graph neural networks. Our method
utilizes Graph Convolution Networks (GCN) and Long Short-Term Memory (LSTM) to
model the spatio-temporal interactions between target vehicles and their
surrounding traffic. To personalize the predictions, we establish a pipeline
that leverages transfer learning: the model is initially pre-trained on a
large-scale trajectory dataset and then fine-tuned for each driver using their
specific driving data. We employ human-in-the-loop simulation to collect
personalized naturalistic driving trajectories and corresponding surrounding
vehicle trajectories. Experimental results demonstrate the superior performance
of our personalized GCN-LSTM model, particularly for longer prediction
horizons, compared to its generic counterpart. Moreover, the personalized model
outperforms individual models created without pre-training, emphasizing the
significance of pre-training on a large dataset to avoid overfitting. By
incorporating personalization, our approach enhances trajectory prediction
accuracy
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
Behaviour prediction function of an autonomous vehicle predicts the future
states of the nearby vehicles based on the current and past observations of the
surrounding environment. This helps enhance their awareness of the imminent
hazards. However, conventional behaviour prediction solutions are applicable in
simple driving scenarios that require short prediction horizons. Most recently,
deep learning-based approaches have become popular due to their superior
performance in more complex environments compared to the conventional
approaches. Motivated by this increased popularity, we provide a comprehensive
review of the state-of-the-art of deep learning-based approaches for vehicle
behaviour prediction in this paper. We firstly give an overview of the generic
problem of vehicle behaviour prediction and discuss its challenges, followed by
classification and review of the most recent deep learning-based solutions
based on three criteria: input representation, output type, and prediction
method. The paper also discusses the performance of several well-known
solutions, identifies the research gaps in the literature and outlines
potential new research directions
Deep learning-based vehicle behaviour prediction for autonomous driving applications : a review
Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their promising performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction in this article. We firstly give an overview of the generic problem of vehicle behavior prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The article also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions
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