Vehicle trajectory prediction based on motion model and maneuver recognition
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Citations
Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
Trajectory Prediction of Turning Vehicles based on Intersection Geometry and Observed Velocities
Vision-based moving target interception with a mobile robot based on motion prediction and online planning
Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor
Bayonet-corpus: a trajectory prediction method based on bayonet context and bidirectional GRU
References
Optimal trajectory generation for dynamic street scenarios in a Frenét Frame
Comparison and evaluation of advanced motion models for vehicle tracking
Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety
Lane change intent prediction for driver assistance: On-road design and evaluation
A Note on the Ratio of Two Normally Distributed Variables
Related Papers (5)
Frequently Asked Questions (14)
Q2. What have the authors stated for future works in "Vehicle trajectory prediction based on motion model and maneuver recognition" ?
Future works include the estimation of the uncertainty along the predicted trajectories in order to estimate the TimeTo-Collision with an associated probability of collision for Collision Warning/Avoidance Systems.
Q3. What is the time interval used to define a unique trajectory?
The time interval ] 0, t(K) ] is then sampled and each sample time is used as maneuver ending time t1 to define a unique trajectory.
Q4. What was the purpose of the experiment?
A prerecorded human driving data in semi-urban conditions was used to test the maneuver recognition algorithm and the trajectory prediction method.
Q5. What is the jerk continuity of the lane?
d1 = d ∗ 1 ḋ1 = 0 d̈1 = 0 s̈1 = a0(11)For a change lane, d∗1 equals plus/minus the lane’s width depending on the direction of the maneuver and is null for a keep lane.
Q6. What is the simplest method for predicting a long term?
The method includes a prediction based on CYRA motion model which is very accurate for a short term and a prediction based on maneuver recognition which is more adapted for longer term prediction.
Q7. What is the lateral component of each trajectory?
The lateral component of each trajectory is of the form:d(t) = c5t 5 + c4t 4 + c3t 3 + c2t 2 + c1t+ c0 (12)Where ci,i={0,1,2,3,4,5} are coefficients.
Q8. What is the method for predicting a turn?
The method consists in mixing trajectory prediction based on maneuver recognition and trajectory prediction based on a motion model.
Q9. What is the lateral component of the trajectories?
The trajectories are then converted to the Cartesian coordinate system (see Appendix-B) and the best one is selected with respect to the cost function described hereafter.
Q10. What is the way to predict a turn?
based on the vehicle current state, the road parameters and the detected maneuver, a set of trajectories are first generated and the best one is selected with respect to a cost function described later.
Q11. What is the trajectories generated in the Frenet frame?
The trajectories are first generated in the Frenet frame along the center line of the current lane of the vehicle (see Fig.3), then converted to the initial Cartesian coordinate system.
Q12. What is the jerk continuity of the trajectories?
All the trajectories have the same initial state which is derived from the current state ζ0 = [x0, y0, θ0, v0, a0, ω0] of the vehicle in the Cartesian frame.
Q13. What is the jerk continuity of the vehicle?
Only the following assumptions are made: at the end state, the vehicle is moving right on the center line of its intended lane (known from the MRM) and has a constant longitudinal acceleration during the maneuver.
Q14. What is the average of the trajectories generated?
For real-time implementation, the complexity of the method can be kept low if the number of generated trajectories remains reasonable and if the curvature of the road is constant (in this case, the transformation from the Frenet frame to the Cartesian frame is trivial).