Real-time Cooperative Motion Planning using Efficient Model Predictive Contouring Control

Pauls, Jan-Hendrik; Boxheimer, Mario; Stiller, Christoph

doi:10.1109/iv51971.2022.9827063

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…For this, in [23] the authors additionally employed the partially observable Markov decision process (POMDP) for decision-making before MPC allowing it to handle more uncertain scenarios. At the same time, to encourage vehicles to collaborate, MPCs that can control multiple AVs within a scenario were developed in [24], [25]. The problem is that it only enables cooperation between AVs and it is difficult to consider other users on the road, needless to say, delivering social-aware driving.…”

Section: Introductionmentioning

confidence: 99%

Social-Aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving Through Roundabouts as a Case Study

Zhang,

Dong,

Farah

et al. 2023

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with humandriven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs' deployment and safe driving under various maneuvers. Previous research mostly focuses on the trajectory planning of AVs using Model Predictive Control or other relevant methods, while seldom considering the integrated planning and control of AVs altogether to simplify the whole pipeline architecture. Furthermore, there are very limited studies on social-aware driving that makes AVs understandable and expected by human drivers, and none when it comes to the challenging maneuver of driving through roundabouts. To fill these research gaps, this paper develops an integrated socialaware planning and control algorithm for AVs' driving through roundabouts based on Driving Risk Field (DRF), Social Value Orientation (SVO), and Model Predictive Contouring Control (MPCC), i.e., DRF-SVO-MPCC. The proposed method is tested and verified with simulation on the open-sourced highway-env platform. Compared with the baseline method using purely Nonlinear Model Predictive Control, the DRF-SVO-MPCC can achieve better performance under various maneuvers of driving through roundabouts with and without surrounding HDVs.

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Section: Introductionmentioning

confidence: 99%