Model predictive path planning with time-varying safety constraints for highway autonomous driving

Jalalmaab, Mehdi; Fi̇dan, Barış; Jeon, Soo; Falcone, Paolo

doi:10.1109/icar.2015.7251458

Cited by 29 publications

(9 citation statements)

References 12 publications

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“…By assuming the worst case scenario, the motion planner is made towards robustness. Jalalmaab et al, [256] designed a model predictive controller (MPC) with timevarying safety constraints for path planning with collision avoidance. The proposed controller takes the road boundary and dynamic change of surrounding vehicles into the constraints consideration and finds the best commands of longitudinal and lateral control to navigate the AV.…”

Section: Planningmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

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Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

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Section: Planningmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The point-mass kinematic equations of motion describing vehicle motion in the longitudinal direction [18] is given by…”

Section: Reachability Analysismentioning

confidence: 99%

Dynamic decision making in lane change: Game theory with receding horizon

Meng¹,

Su²,

Liu³

et al. 2016

2016 UKACC 11th International Conference on Control (CONTROL)

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Decision making for lane change manoeuvre is of practical importance to guarantee a smooth, efficient and safe operation for autonomous driving. It is, however, challenging. On one hand, the behaviours of ego vehicle and adjacent vehicles are dependent and interactive. On the other hand, the decision should strictly guarantee safety during the whole process of lane change with uncertain and incomplete information in a dynamic and cluttered environment. To this end, the concept of Receding Horizon Control (RHC) is integrated into game theory in conjunction with reachability analysis tool, resulting in RHC based game theory. Specifically, the decision of each game relies on not only uncertain information at current step but also the future information calculated by reachability analysis. The decision is repeatedly made with the advent of new information using the concept of RHC. As a result, safety can be guaranteed during the whole process of lane change in a dynamic environment. Case study is conducted to demonstrate the advantages of the proposed approach. It is shown that the proposed RHC based game theory approach incorporating uncertain information can provide a safer and real-time decision.

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“…MPC performs well particularly in dynamic environments by embedding the prediction models of surrounding road users into the constraints of the optimization problem [2]. Consequently, MPC has been applied to complex driving scenarios in the presence of other road users, such as lane changing [3]- [5], obstacle avoidance [6], [7], pedestrian avoidance [8], [9], adaptive cruise control (ACC) [10], and turning and crossing at intersections [11], [12].…”

Section: Introductionmentioning

confidence: 99%

MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Control

Honda¹,

Okuda²,

Itō³

et al. 2022

Preprint

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This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPC) named MPC Builder. In this framework, several components necessary for MPC, such as models, constraints, and cost functions, are prepared in advance. The MPC Builder then online generates various MPCs according to traffic situations in a unified manner. This scheme enabled us to represent various driving tasks with minimal design effort. The proposed framework was implemented considering the continuation/generalized minimum residual (C/GMRES) method optimization solver, which can reduce computational costs. Finally, numerical experiments on multiple driving scenarios were presented.

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Model predictive path planning with time-varying safety constraints for highway autonomous driving

Cited by 29 publications

References 12 publications

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Dynamic decision making in lane change: Game theory with receding horizon

MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Control

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