This paper presents a technique based on the probabilistic road map algorithm for trajectory planning in autonomous driving. The objective is to provide an algorithm allowing to compute the trajectory of the vehicle by reducing the distance traveled and minimizing the lateral deviation and relative yaw angle of the vehicle with respect to the reference trajectory, while maximizing its longitudinal speed. The vehicle is considered as a 3 Degree-of-Freedom bicycle model and a Model Predictive Control algorithm is implemented to control the lateral and longitudinal dynamics. Both the control and trajectory generation algorithms take the road lane boundaries as the only input from the surrounding environment exploiting a simulated camera. The performance of the technique is compared with the case in which the reference trajectory is the central line between the lane boundaries. The proposed algorithm is validated in a simulated driving scenario.

Optimal Trajectory Generation Using an Improved Probabilistic Road Map Algorithm for Autonomous Driving / Feraco, Stefano; Bonfitto, Angelo; Khan, Irfan; Amati, Nicola; Tonoli, Andrea. - ELETTRONICO. - 4:(2020). (Intervento presentato al convegno ASME - 22nd International Conference on Advanced Vehicle Technologies (AVT) tenutosi a Virtual nel 17-19/08/2020) [10.1115/DETC2020-22311].

Optimal Trajectory Generation Using an Improved Probabilistic Road Map Algorithm for Autonomous Driving

Feraco, Stefano;Bonfitto, Angelo;Khan, Irfan;Amati, Nicola;Tonoli, Andrea
2020

Abstract

This paper presents a technique based on the probabilistic road map algorithm for trajectory planning in autonomous driving. The objective is to provide an algorithm allowing to compute the trajectory of the vehicle by reducing the distance traveled and minimizing the lateral deviation and relative yaw angle of the vehicle with respect to the reference trajectory, while maximizing its longitudinal speed. The vehicle is considered as a 3 Degree-of-Freedom bicycle model and a Model Predictive Control algorithm is implemented to control the lateral and longitudinal dynamics. Both the control and trajectory generation algorithms take the road lane boundaries as the only input from the surrounding environment exploiting a simulated camera. The performance of the technique is compared with the case in which the reference trajectory is the central line between the lane boundaries. The proposed algorithm is validated in a simulated driving scenario.
2020
978-0-7918-8393-8
File in questo prodotto:
File Dimensione Formato  
IDETC2020-22311-Optimal Trajectory Generation Using an Improved Probabilistic Road Map Algorithm for Autonomous.pdf

accesso riservato

Descrizione: Articolo principale
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.51 MB
Formato Adobe PDF
1.51 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2851458