A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
Forecasting trajectory and behavior of road-agents using spectral clustering in graph-lstms
We present a novel approach for traffic forecasting in urban traffic scenarios using a
combination of spectral graph analysis and deep learning. We predict both the low-level …
combination of spectral graph analysis and deep learning. We predict both the low-level …
On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data
Trajectories drawn in a common reference system by all the vehicles on a road are the
ultimate empirical data to investigate traffic dynamics. The vast amount of such data made …
ultimate empirical data to investigate traffic dynamics. The vast amount of such data made …
Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios
Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …
especially in dense traffic, because the merging vehicle typically needs to interact with other …
Estimating acceleration and lane-changing dynamics from next generation simulation trajectory data
C Thiemann, M Treiber… - Transportation Research …, 2008 - journals.sagepub.com
The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and
lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and …
lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and …
Real time queue length estimation for signalized intersections using travel times from mobile sensors
We study how to estimate real time queue lengths at signalized intersections using
intersection travel times collected from mobile traffic sensors. The estimation is based on the …
intersection travel times collected from mobile traffic sensors. The estimation is based on the …
Freeway traffic oscillations: microscopic analysis of formations and propagations using wavelet transform
In this paper we identify the origins of stop-and-go (or slow-and-go) driving and measure
microscopic features of their propagations by analyzing vehicle trajectories via Wavelet …
microscopic features of their propagations by analyzing vehicle trajectories via Wavelet …
Physics-informed deep learning for traffic state estimation: Illustrations with LWR and CTM models
We present a physics-informed deep learning (PIDL) approach to tackle the challenge of
data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep …
data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep …
A modified car-following model based on a neural network model of the human driver effects
Nowadays, among the microscopic traffic flow modeling approaches, the car-following
models are increasingly used by transportation experts to utilize appropriate intelligent …
models are increasingly used by transportation experts to utilize appropriate intelligent …
Unmanned aerial vehicle-based traffic analysis: A case study for shockwave identification and flow parameters estimation at signalized intersections
Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles
(UAVs), or drones, have become increasingly popular. However, the civil applications of this …
(UAVs), or drones, have become increasingly popular. However, the civil applications of this …