Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation

Mynuddin, Mohammed; Gao, Weinan

doi:10.1049/iet-its.2019.0404

Cited by 14 publications

(5 citation statements)

References 39 publications

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“…The evaluation function of whether a vehicle has risks of violating a solid lane line, red light, and max speed limit are defined by Equations ( 8)- (10), respectively:…”

Section: Traffic Rulesmentioning

confidence: 99%

“…When addressing the complex challenge of integrated multidemands, the prevailing approach involves formulating it as a multi-objective optimization problem [8,9]. In this context, the cutting-edge methodologies encompass Reinforcement Learning [10][11][12][13] and NMPC [14][15][16]. This paper specifically focuses on NMPC-based methods due to their clarity of mechanism.…”

Section: Introductionmentioning

confidence: 99%

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A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC

Han,

Ma,

Wang

et al. 2023

Applied Sciences

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Nonlinear Model Predictive Control (NMPC) is an effective approach for motion planning in autonomous vehicles that need to satisfy multiple driving demands. Within the realm of planner design, current strategies inadequately address the issues related to redundancy and conflicts among these diverse demands. This shortcoming leads to low efficiency and suboptimal performance, particularly when faced with a high volume of demands. In response to this challenge, this paper introduces the Hierarchical and Multi-Domain (HMD) strategy as a solution for designing a multi-objective NMPC planner. This strategy enables the dynamic adjustment of the integration method for demand indicators based on their priority. To evaluate the risk of breaching driving demands, several risk functions are established. The constraints and objective function of the planner are meticulously designed in accordance with the HMD strategy and evaluation functions. Simulation results attest to the advantages of the HMD-based planner, which, compared to planners based on traditional multi-objective (TMO) strategies, exhibits a 68.5% improvement in solution efficiency and the simultaneous enhancement of driving safety. Additionally, the HMD approach reduces the maximum jerk by 58.8%.

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“…The evaluation function of whether a vehicle has risks of violating a solid lane line, red light, and max speed limit are defined by Equations ( 8)- (10), respectively:…”

Section: Traffic Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC

Han,

Ma,

Wang

et al. 2023

Applied Sciences

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“…The number of severe accidents caused by distractions fluctuated very slightly over this period. While researchers are conducting extensive studies on autonomous vehicles [8]- [11], research on driver distraction remains crucial. In [12], the authors provide a comprehensive review of current research on in-vehicle driver distraction, specifically focusing on mobile phone usage, a topic that has received significant attention in the literature.…”

Section: Literature Review On Driver Distractionmentioning

confidence: 99%

Experimental Studies On Drivers Distractions: Investigating the Effects of Distractions on Driving Performance

Mynuddin,

Sadeeq,

Uddin Khan

et al. 2023

2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific)

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Driving hazards encompass a range of factors, such as poor road conditions, weather, and human behavior. They represent a significant threat to the safety of individuals on the road and are very likely one of the leading causes of traffic accidents. These hazards exist alongside well-known risk factors like driving under the influence of drugs, extreme fatigue, drowsiness, and excessive speeding. Impaired driving, which includes behaviors such as drunk or drugged driving, has persistently plagued American roads for many years. It poses an ongoing risk, not only to the impaired driver but also to other road users and the surrounding infrastructure. The primary objective of this paper is to investigate specific forms of distractions that drivers encounter. These distractions encompass activities such as texting or using a mobile device, eating, conversing with passengers, and paying attention to advertising signs along the roadside. The research aims to understand how each of these distractions affects a driver's ability to operate a vehicle safely. In essence, this chapter delves into the various ways in which these distractions can compromise a driver's attention and, consequently, impact road safety.

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“…These approaches are applicable for cases such as risky height of flight for an airplane; but they are not constructive for applications that the information about dangerous occasions is not available which is somehow inherent to the concept of risk. In some existing results, a soft safety requirement is encoded as a desired objective, such as desired headway in a cruise control problem in which existing tracking control methods can be employed; in Reference 11, an RL algorithm for cruise control is proposed to enhance the transient response in a distributed manner for the vehicular platoon.…”

Section: Introductionmentioning

confidence: 99%

“…Reference 26 incorporates state and input constraints in RL framework using penalty function and barrier function (BF)‐based state transformation; however, possible conflict between safety and stability is not considered. In Reference 11, an RL‐based distributed predictive algorithm for the cruise control system is presented.…”

Section: Introductionmentioning

confidence: 99%

Safe reinforcement learning: A control barrier function optimization approach

Marvi

Kiumarsi

2020

Intl J Robust & Nonlinear

136

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Summary This article presents a learning‐based barrier certified method to learn safe optimal controllers that guarantee operation of safety‐critical systems within their safe regions while providing an optimal performance. The cost function that encodes the designer's objectives is augmented with a control barrier function (CBF) to ensure safety and optimality. A damping coefficient is incorporated into the CBF which specifies the trade‐off between safety and optimality. The proposed formulation provides a look‐ahead and proactive safety planning and results in a smooth transition of states within the feasible set. That is, instead of applying an optimal controller and intervening with it only if the safety constraints are violated, the safety is planned and optimized along with the performance to minimize the intervention with the optimal controller. It is shown that addition of the CBF into the cost function does not affect the stability and optimality of the designed controller within the safe region. This formulation enables us to find the optimal safe solution iteratively. An off‐policy reinforcement learning (RL) algorithm is then employed to find a safe optimal policy without requiring the complete knowledge about the system dynamics, while satisfies the safety constraints. The efficacy of the proposed safe RL control design approach is demonstrated on the lane keeping as an automotive control problem.

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Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation

Cited by 14 publications

References 39 publications

A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC

A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC

Experimental Studies On Drivers Distractions: Investigating the Effects of Distractions on Driving Performance

Safe reinforcement learning: A control barrier function optimization approach

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