1,315 research outputs found
Naturalistic Driver Intention and Path Prediction using Machine Learning
Autonomous vehicles are still yet to be available to the public. This is because there are a number of challenges that have not been overcome to ensure that autonomous vehicles can safely and efficiently drive on public roads. Accurate prediction of other vehicles is vital for safe driving, as interacting with other vehicles is unavoidable on public streets. This thesis explores reasons why this problem of scene understanding is still unsolved, and presents methods for driver intention and path prediction. The thesis focuses on intersections, as this is a very complex scenario in which to predict the actions of human drivers. There is very limited data available for intersection studies from the perspective of an autonomous vehicle. This thesis presents a very large dataset of over 23,000 vehicle trajectories, used to validate the algorithms presented in this thesis. This dataset was collected using a lidar based vehicle detection and tracking system onboard a vehicle. Analytics of this data is presented. To determine the intent of vehicle at an intersection, a method for manoeuvre classification through the use of recurrent neural networks is presented. This allows accurate predictions of which destination a vehicle will take at an unsignalised intersection, based on that vehicle's approach. The final contribution of this thesis presents a method for driver path prediction, based on recurrent neural networks. It produces a multi-modal prediction for the vehicle’s path with uncertainty assigned to each mode. The output modes are not hand labelled, but instead learned from the data. This results in there not being a fixed number of output modes. Whilst the application of this method is vehicle prediction, this method shows significant promise to be used in other areas of robotics
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
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