scholarly journals Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks

2019 ◽  
pp. 148-160 ◽  
Author(s):  
Ferenc Hegedüs ◽  
Tamás Bécsi ◽  
Szilárd Aradi ◽  
Péter Gáspár
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Motion Planning ◽  
Nonlinear Optimization ◽  
Hybrid Approach ◽  
Road Vehicles ◽  
Artificial Neural

scholarly journals Fast Motion Model of Road Vehicles with Artificial Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 928
Author(s):  
Ferenc Hegedüs ◽  
Péter Gáspár ◽  
Tamás Bécsi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dynamic Model ◽  
Piecewise Linear ◽  
Complex Problem ◽  
Computational Effort ◽  
Road Vehicles ◽  
Vehicle Motion ◽  
Artificial Neural ◽  
Original Dynamic

Nonlinear optimization-based motion planning algorithms have been successfully used for dynamically feasible trajectory planning of road vehicles. However, the main drawback of these methods is their significant computational effort and thus high runtime, which makes real-time application a complex problem. Addressing this field, this paper proposes an algorithm for fast simulation of road vehicle motion based on artificial neural networks that can be used in optimization-based trajectory planners. The neural networks are trained with supervised learning techniques to predict the future state of the vehicle based on its current state and driving inputs. Learning data is provided for a wide variety of randomly generated driving scenarios by simulation of a dynamic vehicle model. The realistic random driving maneuvers are created on the basis of piecewise linear travel velocity and road curvature profiles that are used for the planning of public roads. The trained neural networks are then used in a feedback loop with several variables being calculated by additional numerical integration to provide all the outputs of the original dynamic model. The presented model can be capable of short-term vehicle motion simulation with sufficient precision while having a considerably faster runtime than the original dynamic model.

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