DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Seulbin | ko |
dc.contributor.author | Lee, Kibeom | ko |
dc.contributor.author | Jeon, Hyeongseok | ko |
dc.contributor.author | Kum, Dongsuk | ko |
dc.date.accessioned | 2022-10-30T01:01:32Z | - |
dc.date.available | 2022-10-30T01:01:32Z | - |
dc.date.created | 2022-04-04 | - |
dc.date.created | 2022-04-04 | - |
dc.date.created | 2022-04-04 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.23, no.10, pp.17594 - 17606 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10203/299168 | - |
dc.description.abstract | Lane-merging scenarios pose highly challenging problems for autonomous vehicles due to conflicts of interest between the human-driven and cutting-in autonomous vehicles. Such conflicts become severe when traffic increases, and cut-in algorithms suffer from a steep trade-off between safety and cut-in performance. In this study, a reinforcement learning (RL)-based cut-in policy network nested within a finite state machine (FSM)--which is a high-level decision maker, is proposed to achieve high cut-in performance without sacrificing safety. This FSM-RL hybrid approach is proposed to obtain 1) a strategic and adjustable algorithm, 2) optimal safety and cut-in performance, and 3) robust and consistent performance. In the high-level decision making algorithm, the FSM provides a framework for four cut-in phases (ready for safe gap selection, gap approach, negotiation, and lane-change execution) and handles the transitions between these phases by calculating the collision risks associated with target vehicles. For the lane-change phase, a policy-based deep-RL approach with a soft actor-critic network is employed to get optimal cut-in performance. The results of simulations show that the proposed FSM-RL cut-in algorithm consistently achieves a high cut-in success rate without sacrificing safety. In particular, as the traffic increases, the cut-in success rate and safety are significantly improved over existing optimized rule-based cut-in algorithms and end-to-end RL algorithm. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Autonomous Vehicle Cut-In Algorithm for Lane-Merging Scenarios via Policy-Based Reinforcement Learning Nested Within Finite-State Machine | - |
dc.type | Article | - |
dc.identifier.wosid | 000767805200001 | - |
dc.identifier.scopusid | 2-s2.0-85126332850 | - |
dc.type.rims | ART | - |
dc.citation.volume | 23 | - |
dc.citation.issue | 10 | - |
dc.citation.beginningpage | 17594 | - |
dc.citation.endingpage | 17606 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | - |
dc.identifier.doi | 10.1109/TITS.2022.3153848 | - |
dc.contributor.localauthor | Kum, Dongsuk | - |
dc.contributor.nonIdAuthor | Hwang, Seulbin | - |
dc.contributor.nonIdAuthor | Lee, Kibeom | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Safety | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordAuthor | Autonomous vehicles | - |
dc.subject.keywordAuthor | Vehicles | - |
dc.subject.keywordAuthor | Decision making | - |
dc.subject.keywordAuthor | Automata | - |
dc.subject.keywordAuthor | Stochastic processes | - |
dc.subject.keywordAuthor | Autonomous vehicle | - |
dc.subject.keywordAuthor | lane-merge | - |
dc.subject.keywordAuthor | cut-in | - |
dc.subject.keywordAuthor | deep reinforcement learning | - |
dc.subject.keywordAuthor | finite-state machine | - |
dc.subject.keywordPlus | CHANGING MODELS | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.