Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data
By Christian Thiemann, Martin Treiber, Arne Kesting. 2008
407 citations recorded by the source corpus.
The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and acceleration information cannot be extracted directly because the noise in the NGSIM positional information is greatly increased by the necessary numerical differentiations. A smoothing algorithm is proposed for positions, velocities, and accelerations that can also be applied near the boundaries. The smoothing time interval is estimated on the basis of velocity time series and the variance of the processed acceleration time series. The velocity information obtained in this way is then applied to calculate the density function of the two-dimensional distribution of velocity and inverse distance and the density of the distribution corresponding to the “microscopic” fundamental diagram. It is also used to calculate the distributions of time gaps and times to collision, conditioned to several ranges of velocities and velocity differences. By simulating virtual stationary detectors, it is shown that the probability for critical values of the times to collision is greatly underestimated when estimated from single-vehicle data of stationary detectors. Finally, the lane-changing process is investigated, and a quantitative criterion is formulated for the duration of lane changes that is based on the trajectory density in normalized coordinates. There is a noisy but significant velocity advantage in favor of the targeted lane that decreases immediately before the change due to anticipatory accelerations.
Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/c90904f0b2bbda1e970aced4d636167d6cd64879
How researchers see this paper
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In Automotive Engineering
- Arne Kesting
- Martin Treiber
- Lizhe Li
- Benjamin Coifman
- Szilárd Aradi
- Zuduo Zheng
- Mohammad Saifuzzaman
In Control and Systems Engineering
- Arne Kesting
- Martin Treiber
- Lizhe Li
- Benjamin Coifman
- Dirk Helbing
- Zuduo Zheng
- Mohammad Saifuzzaman
- Alexandre M. Bayen
In Safety, Risk, Reliability and Quality
Citations in the corpus
- A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset (2017, background): “…minority of researchers who have found unrealistic relationships in the NGSIM data and now question the accuracy of the NGSIM trajectories, e.g., Thiemann et al. (2008), Hamdar and Mahmassani (2008), and Duret et al. (2008) found noise in the instantaneous position, speed, and/or acceleration…”
- Microscopic Calibration and Validation of Car-Following Models – A Systematic Approach (2013, background): “Trajectory data such as these of the NGSIM project [11, 12] contain the dynamics of the positions (and lanes) of all vehicles in a given spatiotemporal region.”
- Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks (2018, background): “from considerable tracking noise [20], and even with aggressive filtering and correction techniques, significant problems still exist such as multiple collisions [21] that do not exist in the real-world sample.”