369 research outputs found
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
As autonomous vehicles (AVs) need to interact with other road users, it is of
importance to comprehensively understand the dynamic traffic environment,
especially the future possible trajectories of surrounding vehicles. This paper
presents an algorithm for long-horizon trajectory prediction of surrounding
vehicles using a dual long short term memory (LSTM) network, which is capable
of effectively improving prediction accuracy in strongly interactive driving
environments. In contrast to traditional approaches which require trajectory
matching and manual feature selection, this method can automatically learn
high-level spatial-temporal features of driver behaviors from naturalistic
driving data through sequence learning. By employing two blocks of LSTMs, the
proposed method feeds the sequential trajectory to the first LSTM for driver
intention recognition as an intermediate indicator, which is immediately
followed by a second LSTM for future trajectory prediction. Test results from
real-world highway driving data show that the proposed method can, in
comparison to state-of-art methods, output more accurate and reasonable
estimate of different future trajectories over 5s time horizon with root mean
square error (RMSE) for longitudinal and lateral prediction less than 5.77m and
0.49m, respectively.Comment: Published at the 21st International Conference on Intelligent
Transportation Systems (ITSC), 201
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
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
The ability to accurately predict the trajectory of surrounding vehicles is a
critical hurdle to overcome on the journey to fully autonomous vehicles. To
address this challenge, we pioneer a novel behavior-aware trajectory prediction
model (BAT) that incorporates insights and findings from traffic psychology,
human behavior, and decision-making. Our model consists of behavior-aware,
interaction-aware, priority-aware, and position-aware modules that perceive and
understand the underlying interactions and account for uncertainty and
variability in prediction, enabling higher-level learning and flexibility
without rigid categorization of driving behavior. Importantly, this approach
eliminates the need for manual labeling in the training process and addresses
the challenges of non-continuous behavior labeling and the selection of
appropriate time windows. We evaluate BAT's performance across the Next
Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD),
and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its
superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of
prediction accuracy and efficiency. Remarkably, even when trained on reduced
portions of the training data (25%), our model outperforms most of the
baselines, demonstrating its robustness and efficiency in predicting vehicle
trajectories, and the potential to reduce the amount of data required to train
autonomous vehicles, especially in corner cases. In conclusion, the
behavior-aware model represents a significant advancement in the development of
autonomous vehicles capable of predicting trajectories with the same level of
proficiency as human drivers. The project page is available at
https://github.com/Petrichor625/BATraj-Behavior-aware-Model
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Lane change (LC) is one of the safety-critical manoeuvres in highway driving
according to various road accident records. Thus, reliably predicting such
manoeuvre in advance is critical for the safe and comfortable operation of
automated driving systems. The majority of previous studies rely on detecting a
manoeuvre that has been already started, rather than predicting the manoeuvre
in advance. Furthermore, most of the previous works do not estimate the key
timings of the manoeuvre (e.g., crossing time), which can actually yield more
useful information for the decision making in the ego vehicle. To address these
shortcomings, this paper proposes a novel multi-task model to simultaneously
estimate the likelihood of LC manoeuvres and the time-to-lane-change (TTLC). In
both tasks, an attention-based convolutional neural network (CNN) is used as a
shared feature extractor from a bird's eye view representation of the driving
environment. The spatial attention used in the CNN model improves the feature
extraction process by focusing on the most relevant areas of the surrounding
environment. In addition, two novel curriculum learning schemes are employed to
train the proposed approach. The extensive evaluation and comparative analysis
of the proposed method in existing benchmark datasets show that the proposed
method outperforms state-of-the-art LC prediction models, particularly
considering long-term prediction performance.Comment: 13 pages, 11 figure
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
A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
Anticipating the motion of other road users is crucial for automated driving
systems (ADS), as it enables safe and informed downstream decision-making and
motion planning. Unfortunately, contemporary learning-based approaches for
motion prediction exhibit significant performance degradation as the prediction
horizon increases or the observation window decreases. This paper proposes a
novel technique for trajectory prediction that combines a data-driven
learning-based method with a velocity vector field (VVF) generated from a
nature-inspired concept, i.e., fluid flow dynamics. In this work, the vector
field is incorporated as an additional input to a convolutional-recurrent deep
neural network to help predict the most likely future trajectories given a
sequence of bird's eye view scene representations. The performance of the
proposed model is compared with state-of-the-art methods on the HighD dataset
demonstrating that the VVF inclusion improves the prediction accuracy for both
short and long-term (5~sec) time horizons. It is also shown that the accuracy
remains consistent with decreasing observation windows which alleviates the
requirement of a long history of past observations for accurate trajectory
prediction. Source codes are available at:
https://github.com/Amir-Samadi/VVF-TP.Comment: This paper has been accepted and nominated as the best student paper
at the 26th IEEE International Conference on Intelligent Transportation
Systems (ITSC 2023
Edge Learning of Vehicular Trajectories at Regulated Intersections
Trajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology
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