[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84150-en":3,"doc-seo-84150-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84150,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Integrated Automated Car Following and Lane-changing Control Based on a Parametrized Deep Q-network with Hybrid Action Space","Lane-changing triggers traffic disturbances that can reduce upstream safety and efficiency, especially when coupled with car-following under congestion. The study introduces an integrated control framework for connected and automated vehicles that unifies lane-changing and car-following rather than treating them as separate tasks. A Parametrized Deep Q-network with a hybrid action space jointly selects discrete lane-change decisions and continuous acceleration commands (lateral and longitudinal). Training maximizes cumulative reward to improve safety and efficiency while maintaining comfort, outperforming separated approaches in numerical experiments.","arXiv :2607 .06771v1 [ ee ss . SY] 7 Jul 2026  \nIntegrated Automated Car Following and Lane-changing control based on a Parametrized Deep Q-network with Hybrid  \nAction Space  \nHao Zhanga , Zihao Lia , Yang Zhoua  \na Zachry Department of Civil & Environmental Engineering, Texas A&M University, College  \nStation, TX 77843, USA,  \nAbstract  \nLane-change, a triggering of traffic disturbances to the upstream vehicles, is detrimental to traffic safety and efficiency. Coupled with car following behavior, the joint maneuvers depict the general picture of how traffic disturbances generate and propagate through vehicle streams, especially under traffic congestion. This study proposes an integrated control framework for lane-changing and car-following for connected and automated vehicles (CAVs), where those two tasks are largely treated as independent driving tasks by prevailing methods. Utilizing the Parametrized Deep Q-Network (P-DQN) with a hybrid action space, the framework adeptly models multiple objectives in CAV control. The P-DQN’s high-level control is employed for discrete lane change decisions, while its low-level control manages continuous acceleration actions (i.e., lateral and longitudinal) . These actions are interdependently determined, seamlessly integrating car-following and lane-changing control. By training to maximize cumulative rewards, the proposed control strategy ensures driving safety as well asthe efficiency of car-following, lane-changing, and lane-keeping. Through numerical experiments, it is indicated that the P-DQN outperforms separated control methods (e.g., the combination of Minimizing Overall Braking Decelerations Induced by Lane Changes (MOBIL) model and the Intelligent Driver Model (IDM)) in terms of safety and comfort.  \nKeywords: CAVs, automated lane-changing, car following control, parametrized deep Q-network  \n1. Introduction  \nThe advent of autonomous driving technology has brought forth a myriad of opportunities for enhancing road safety and traffic efficiency (Li et al., 2024) . Central  \nPreprint submitted to Neurocomputing July 9, 2026  \nto this technological evolution is the need for sophisticated control strategies capable of making reliable decision in complex driving scenarios. Lane changing behavior, a critical aspect of automated vehicle control due to its complexity and the need for precise decision making in dynamic traffic condition, directly impacts safety and traffic flow efficiency (Elallid et al., 2022; Li et al., 2022a; Nian et al., 2020; Zhang et al., 2023a,b) . The traditional approaches to model lane-changing behaviors mainly involve pre-defined rules and explicitly designed models, such as Minimizing Overall Braking decelerations Induced by Lane changes (MOBIL) (Wang et al., 2015) . MOBIL utilizes a utility function to determine whether a vehicle should change lanes. The function examines the benefits and potential negative impacts of a lane change, focusing on its effects on both the vehicle initiating the change and the surrounding traffic. Additionally, several studies have proposed the concept of a virtual lanechange trajectory, which vehicle can follow during the lane changing process (Choi et al., 2015; Ho et al., 2009) . They usually utilize mathematical functions such as quintic polynomial, sinusoidal, and trapezoidal to approximate lane-change trajectories (Zhou et al., 2017) . While these models perform reasonably well in designed scenarios or within the model’s boundaries, they are insufficient for handling situations beyond the defined range (Ammourah and Talebpour, 2023) . For example, MOBIL is incapable of planning for a further lane-change maneuver (Treiber and Helbing, 2016) . These models cannot fully describe the lane-changing behavior, because the lane-changing decision is related to multiple factors, including safety considerations, driver comfort, and the efficiency of traffic flow. Relying solely on predefined rules and explicit models in connected aut","cbCaijUj3ER4FiKg","https://ap.wps.com/l/cbCaijUj3ER4FiKg","pdf",4980997,1,26,"English","en",105,"# Introduction\n## Background and Motivation\n## Related Work: Rule-Based and Model-Based Lane Changing\n## Related Work: Reinforcement Learning for Lane Changing\n## Problem Setup and Research Gap","[{\"question\":\"What problem does the integrated framework address?\",\"answer\":\"It addresses how to jointly control lane-changing and car-following so that traffic disturbances propagate safely and efficiently through vehicle streams, particularly under congestion.\"},{\"question\":\"How does the Parametrized Deep Q-network with hybrid action space work?\",\"answer\":\"Its high-level control outputs discrete lane-change decisions, while its low-level control outputs continuous acceleration actions, with lateral and longitudinal components determined interdependently.\"},{\"question\":\"What performance benefit is reported compared with separated control methods?\",\"answer\":\"Numerical experiments indicate the proposed P-DQN approach outperforms separated methods (e.g., MOBIL+IDM combinations) in safety and comfort.\"}]",1784193457,66,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"integrated-automated-car-following-and-lane-changing-control-based-on-a-parametrized-deep-q-network-with-hybrid-action-space","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/integrated-automated-car-following-and-lane-changing-control-based-on-a-parametrized-deep-q-network-with-hybrid-action-space/84150/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the integrated framework address?","Question",{"text":75,"@type":76},"It addresses how to jointly control lane-changing and car-following so that traffic disturbances propagate safely and efficiently through vehicle streams, particularly under congestion.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the Parametrized Deep Q-network with hybrid action space work?",{"text":80,"@type":76},"Its high-level control outputs discrete lane-change decisions, while its low-level control outputs continuous acceleration actions, with lateral and longitudinal components determined interdependently.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance benefit is reported compared with separated control methods?",{"text":84,"@type":76},"Numerical experiments indicate the proposed P-DQN approach outperforms separated methods (e.g., MOBIL+IDM combinations) in safety and 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