Autonomous overtaking using stochastic model predictive control

Nguyễn, Ngọc Ánh; Moser, Dominik A.; Schrangl, Patrick; Re, Luigi del; Jones, Stephen

doi:10.1109/ascc.2017.8287308

Cited by 19 publications

(8 citation statements)

References 13 publications

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“…Other studies such as [7] contributed to the subject by proposing a vehicle detection assistance system, that uses appearances to detect overtaking and receding vehicles. Concerning the actual maneuvers themselves, different control laws were proposed to govern the maneuvers in autonomous vehicles ( [8], [9]). The former studies deal with the maneuvers from the subject vehicle's perspective, and do not treat the cooperative aspect.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Assaad

Talj

Charara

2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Vehicle automation opened the door for a myriad of new applications that target vehicles. Longitudinal maneuvers are well studied in the literature, whereas lateral maneuvers studies are scarce in comparison. Maneuvers such as lane changing on highways are complex, and require gathering information about the different neighbors in the vicinity of the subject vehicle. Vehicle-to-vehicle (V2V) communications provided new ways for acquiring such information. Cooperation is proven to be useful when performing such maneuvers. Using V2V communication, and Systems of Systems (SoS) principles, this paper presents a Cooperative Lateral Maneuvers Manager (CLMM), an integrated system in autonomous vehicles, that implements the requirements of the Cooperative maneuvers manager for autonomous vehicles (CMMAV), a SoS that enables cooperation between autonomous vehicles to perform their desired lateral maneuvers. CLMM was validated using Anylogic * , a multi-agent simulator, and the results show that it respects the requirements of the system.

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Section: Related Workmentioning

confidence: 99%

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Assaad

Talj

Charara

2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…Basically, the TFSS is more efficient at simulating microscopic traffic flow while it ignores the vehicle dynamic details; on the contrary, the IPG CarMaker can better simulate the vehicle dynamics such as the powertrain system and sensor system; while the Matlab/ SIMULINK is mainly for algorithm optimization purpose, its embedded mathematical toolboxes can be used to resolve optimization problems and generate optimized parameters/ outputs of control algorithms. When complicated traffic flow simulation is not necessary, IPG CarMaker is often used to verify CAV control algorithms, such as longitudinal cruise control (Kuutti et al, 2019), lateral lane change (Samiee et al, 2016), overtaking path planning (Nguyen et al, 2017) and tactical behavior planning (Sefati et al, 2017); while the CAV algorithm is required to be tested in certain traffic flow conditions, a co-simulation between CarMaker, TFSS and Matlab/SIMULINK is often used (Madhusudhanan, 2019;Nalic et al, 2020a;Nalic et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Yue

Abdel-Aty

Wang

2021

JICV

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Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

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“…This formulation makes SMPC particularly well suited to deal with the uncertain urban framework, since the choice of the acceptable risk level allows to balance the trade-off between safety and efficiency. SMPC is widely used for autonomous driving in highway-like scenarios [7], [8], [9], but little attention is dedicated to the typical urban framework, where stops are required to avoid collisions with crossing pedestrians or upcoming vehicles at intersections. Moreover, it is beneficial to take a long prediction horizon into account, allowing to reveal superior maneuver choices, which would prove to be efficient in the long run.…”

Section: Introductionmentioning

confidence: 99%

Multistage Stochastic Model Predictive Control for Urban Automated Driving

Benciolini,

Brüdigam,

Leibold

2021

Preprint

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Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion planning. Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently low. For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately. A highlevel layer takes advantage of a long prediction horizon and of an abstract model to plan the optimal maneuver, and a lower level is in charge of executing the selected maneuver by properly planning the vehicle's trajectory. Numerical simulations are included, showing the potential of our proposal.

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Autonomous overtaking using stochastic model predictive control

Cited by 19 publications

References 13 publications

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Cooperative Lateral Maneuvers Manager for Multi-autonomous Vehicles

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Multistage Stochastic Model Predictive Control for Urban Automated Driving

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