Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model
dc.contributor.author | Jayaraman, Suresh | |
dc.contributor.author | Robert, Lionel + "Jr" | |
dc.contributor.author | Yang, Xi Jessie | |
dc.contributor.author | Pradhan, Anuj | |
dc.contributor.author | Tilbury, Dawn | |
dc.date.accessioned | 2020-03-08T21:45:10Z | |
dc.date.available | 2020-03-08T21:45:10Z | |
dc.date.issued | 2020-03-08 | |
dc.identifier.citation | Jayaraman, S., Robert, L.P., Yang, X.Y., Pradhan, A., Tilbury, D. Efficient Behavior-Aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model, Proceedings of the American Control Conference, July 1-3, 2020, Denver, CO, USA. | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/154113 | |
dc.description.abstract | For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians’ crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavior aware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior using a previously developed pedestrian crossing model. The model incorporates pedestrians’ gap acceptance behavior and utilizes minimal pedestrian information, namely their position and speed, to predict pedestrians’ crossing behaviors. The BMPC controller is validated through simulations and compared to a rule-based controller. By incorporating predictions of pedestrian behavior, the B-MPC controller is able to efficiently plan for longer horizons and handle a wider range of pedestrian interaction scenarios than the rule-based controller. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios. | en_US |
dc.description.sponsorship | Automotive Research Center (ARC) at the University of Michigan, with funding from government contract DoD-DoA W56HZV14-2-0001, through the U.S. Army Combat Capabilities Development Command (CCDC) /Ground Vehicle Systems Center (GVSC). | en_US |
dc.description.sponsorship | National Science Foundation | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ACC 2020 | en_US |
dc.subject | automated vehicles | en_US |
dc.subject | human–automation interaction | en_US |
dc.subject | implicit communication | en_US |
dc.subject | virtual reality | en_US |
dc.subject | Pedestrian | en_US |
dc.subject | Pedestrian automated vehicle interactions | en_US |
dc.subject | Automated Vehicle Interactions | en_US |
dc.subject | self driving cars | en_US |
dc.subject | pedestrians’ gap acceptance | en_US |
dc.subject | hybrid systems model | en_US |
dc.subject | hybrid model of AV interactions | en_US |
dc.subject | immersive virtual environment | en_US |
dc.subject | Pedestrian Crosswalk Behavior | en_US |
dc.subject | Pedestrian vehicle interactions | en_US |
dc.subject | long-term pedestrian trajectory prediction | en_US |
dc.subject | autonomous vehicles | en_US |
dc.subject | autonomous urban driving | en_US |
dc.subject | behavior-aware control | en_US |
dc.subject | autonomous control | en_US |
dc.subject | social human-robot interaction | en_US |
dc.title | Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model | en_US |
dc.type | Conference Paper | en_US |
dc.subject.hlbsecondlevel | Information and Library Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Information, School of | en_US |
dc.contributor.affiliationum | College of Engineering | en_US |
dc.contributor.affiliationum | Robotics Institute | en_US |
dc.contributor.affiliationother | University of Massachusetts Amherst | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/154113/1/Jayaraman_etal_ACC_2020__Behavior_aware_controller_final.pdf | |
dc.identifier.source | Proceedings of the American Control Conference | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.description.filedescription | Description of Jayaraman_etal_ACC_2020__Behavior_aware_controller_final.pdf : MainFile | |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.owningcollname | Information, School of (SI) |
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