Autonomous lane tracking reflecting skilled/un-skilled driving characteristics

IEEE Trans. Intell. Veh.

2022

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Section: Introductionmentioning

confidence: 99%

“…Return second element of Smin as next control input. 16: end for implemented using a CPU based algorithm. The steps are expressed in Algorithm 1.…”

mentioning

confidence: 99%

Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving

Muraleedharan

IEEE Trans. Intell. Veh.

2022

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“…[12]- [14], [20], [27]- [29]. The proposed architecture is based on a nonlinear model predictive control (MPC) [6], [9], [30]- [32] with a switched cost function, and it can find not only smooth path and control input of the vehicle but also the instant of initiating the lane-changing behavior and the place to merge on the target lane. In the proposed MPC, some weighting parameters and the reference position (not path) on the lane, which is specified in the cost function, are switched depending on the driving situation.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Realization of Decision, Planning and Control for Lane-Changing Behavior Using Nonlinear Model Predictive Control

Sugie

IEICE Trans. Inf. & Syst.

et al. 2020

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Path planning and motion control are fundamental components to realize safe and reliable autonomous driving. The discrimination of the role of these two components, however, is somewhat obscure because of strong mathematical interaction between these two components. This often results in a redundant computation in the implementation. One of attracting idea to overcome this redundancy is a simultaneous path planning and motion control (SPPMC) based on a model predictive control framework. SPPMC finds the optimal control input considering not only the vehicle dynamics but also the various constraints which reflect the physical limitations, safety constraints and so on to achieve the goal of a given behavior. In driving in the real traffic environment, decision making has also strong interaction with planning and control. This is much more emphasized in the case that several tasks are switched in some context to realize higherlevel tasks. This paper presents a basic idea to integrate decision making, path planning and motion control which is able to be executed in realtime. In particular, lane-changing behavior together with the decision of its initiation is selected as the target task. The proposed idea is based on the nonlinear model predictive control and appropriate switching of the cost function and constraints in it. As the result, the decision of the initiation, planning, and control of the lane-changing behavior are achieved by solving a single optimization problem under several constraints such as safety. The validity of the proposed method is tested by using a vehicle simulator.

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Realtime collision avoidance control based on continuation method for nonlinear model predictive control with safety constraint

Sugie

2017 11th Asian Control Conference (ASCC)

2017