Infrastructure-Based Vehicle Maneuver Estimation with Intersection-Specific Models

Framing, Christian-Eike; HeBeler, Frank-Josef; Abel, Dirk

doi:10.1109/med.2018.8443069

Cited by 5 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, given the observation sequence the underlying maneuver can be estimated first. This has been encoded using a DBN [25], a set of HMMs [26] or a set of regression models [27]. In the case of HMM or mixed-regression models, a single model was associated with each maneuver and trained with the recorded maneuver-specific trajectory clusters.…”

Section: ) Maneuver Driven Trajectory Predictionmentioning

confidence: 99%

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Mukherjee

Wallace

Wang

2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

We present a Long Short Term Memory (LSTM) encoder-decoder architecture to anticipate the future positions of vehicles in a road network given several seconds of historical observations and associated map features. Unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data for accurate prediction in longer time horizons. It seamlessly performs four tasks: the first task encodes a feature given the past observations, the second task estimates future maneuvers given the encoded state, the third task predicts the future motion given the estimated maneuvers and the initially encoded states, and the fourth task estimates future trajectory given the encoded state and the predicted maneuvers and motions. Experiments on a public benchmark and a new, publicly available radar dataset demonstrate that our approach can equal or surpass the state-of-the-art for long term trajectory prediction.

show abstract

Section: ) Maneuver Driven Trajectory Predictionmentioning

confidence: 99%

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Mukherjee

Wallace

Wang

2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…Therefore, we first recruited 20 experienced drivers (mainly considering age, driving experience, and occupation) and carried out a real vehicle experiment on six curves (each curve consists of one circular curve segment and two straight line segments) with different lengths and curvatures, and collected driving data, including vehicle speeds, trajectories, and steering wheel angles. Then, the Frenet frame 25,26 was used to process the collected driving data. It was interesting to observe that the steering wheel angles of experienced drivers on the curved roads can be considered as a polygonal line.…”

Section: Introductionmentioning

confidence: 99%

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Jiang

Zhou

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

show abstract

Automatic Bi-LSTM Architecture Search Using Bayesian Optimisation for Vehicle Activity Recognition

Radhakrishnan,

AlZoubi

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Infrastructure-Based Vehicle Maneuver Estimation with Intersection-Specific Models

Cited by 5 publications

References 11 publications

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Automatic Bi-LSTM Architecture Search Using Bayesian Optimisation for Vehicle Activity Recognition

Contact Info

Product

Resources

About