3,135 research outputs found
LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application
The development of Adaptive Cruise Control (ACC) systems aims to enhance the
safety and comfort of vehicles by automatically regulating the speed of the
vehicle to ensure a safe gap from the preceding vehicle. However, conventional
ACC systems are unable to adapt themselves to changing driving conditions and
drivers' behavior. To address this limitation, we propose a Long Short-Term
Memory (LSTM) based ACC system that can learn from past driving experiences and
adapt and predict new situations in real time. The model is constructed based
on the real-world highD dataset, acquired from German highways with the
assistance of camera-equipped drones. We evaluated the ACC system under
aggressive lane changes when the side lane preceding vehicle cut off, forcing
the targeted driver to reduce speed. To this end, the proposed system was
assessed on a simulated driving environment and compared with a feedforward
Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model.
The results show that the LSTM-based system is 19.25% more accurate than the
ANN model and 5.9% more accurate than the MPC model in terms of predicting
future values of subject vehicle acceleration. The simulation is done in
Matlab/Simulink environment
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Recommended from our members
A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
- …