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

Okuda, Hiroyuki; Sugie, Nobuto; Suzuki, Tatsuya

doi:10.1109/ascc.2017.8287322

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…In the present work based on the developed vehicular trajectory data; program was coded using a tool, named MATLAB. With the help of available literature (Savolainen et al 2012;Okuda, Sugie, and Suzuki 2018;Bärgman, Smith, and Werneke 2015), a surrounding zone of l = 30 m and m = 2.5 m, was specified and surrounding vehicles are identified at each instant of the time frame for the subject vehicle. Under homogeneous traffic conditions, where car-following models are mostly calibrated irrespective of the vehicle category.…”

Section: Calibration Of Customized Driving Behavior Modelsmentioning

confidence: 99%

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Raju

Arkatkar

Easa

et al. 2021

Transportmetrica B: Transport Dynamics

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This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles' movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models.

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Section: Calibration Of Customized Driving Behavior Modelsmentioning

confidence: 99%

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Raju

Arkatkar

Easa

et al. 2021

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

show abstract

“…4. It is desirable if it is possible to consider the constraint with 'if' condition and the logical operations from the viewpoint of the describability than using the approximated simple shape to express the prohibited area as shown in [27].…”

Section: Constraints For Safety Drivingmentioning

confidence: 99%

“…On the other hand, some literatures which tried SPPMC based on the system control technique could not handle the decision making in the framework because the decision making part is usually discrete time event and handled out of the SPPMC. [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.…”

Section: Introductionmentioning

confidence: 99%

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

Okuda

Sugie

Suzuki

et al. 2020

IEICE Trans. Inf. & Syst.

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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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“…During the studies on SMPLC by the authors in [17] and [18], real-time implementation of SMPLC was found to be facing various challenges. The major challenge was due to the computational complexity in solving an SMPLC problem.…”

Section: Introductionmentioning

confidence: 99%

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

Muraleedharan

Okuda

Suzuki

2022

IEEE Trans. Intell. Veh.

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

Cited by 6 publications

References 6 publications

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

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

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

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