Ph.D. Student · RWTH Aachen University

Jianye Xu

Hello! I'm Jianye, a Ph.D. student at the Chair of Embedded Software (Informatik 11), Department of Computer Science, RWTH Aachen University, supervised by Prof. Bassam Alrifaee.

My research focuses on learning- and optimization-based distributed multi-agent decision-making. During my Ph.D. studies, I am particularly interested in the interplay between reinforcement learning and control barrier functions, with applications to connected and automated vehicles.

Jianye Xu

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Publications

My research connects multi-agent reinforcement learning, optimal control, and connected and automated vehicles, from decision-making in simulation to safe robotic systems.

★ Selected work on pale blue · Gray titles mark preprints. Papers listed by year, newest first.

12 publications

Comparison of high-order and truncated Taylor control barrier functions

★TTCBF: A Truncated Taylor Control Barrier Function for High-Order Safety Constraints

Jianye Xu, Bassam Alrifaee

arXiv preprint arXiv:2601.15196 · 2026

PreprintSafe Control
BibTeX
Download .bib
@article{xu2026ttcbf,
 archiveprefix = {arXiv},
 author = {Xu, Jianye and Alrifaee, Bassam},
 doi = {10.48550/arXiv.2601.15196},
 eprint = {2601.15196},
 journal = {arXiv preprint arXiv:2601.15196},
 primaryclass = {eess},
 publisher = {arXiv},
 title = {TTCBF: A Truncated Taylor Control Barrier Function for High-Order Safety Constraints},
 year = {2026}
}
About this paper

High-Order Control Barrier Functions (HOCBFs) handle safety constraints with relative degree greater than one by introducing a chain of auxiliary functions. This requires multiple class K functions, whose number and tuning burden grow with the relative degree. Because their parameters affect the conservatism and feasibility of the resulting control constraint, tuning them can complicate controller design. We introduce a Truncated Taylor Control Barrier Function (TTCBF), which extends the discrete-time CBF formulation to high-order safety constraints using a single class K function, independent of relative degree. We also propose an adaptive variant, aTTCBF, which optimizes the class K gain online. This paper updates our earlier TTCBF preprint, which remains available at its original arXiv record.

Vehicle trajectories in the initial setup, SigmaRL simulation, CPM Lab digital twin, and physical experiment

Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab

Julius Beerwerth, Jianye Xu, Simon Schäfer, Fynn Belderink, Bassam Alrifaee

at - Automatisierungstechnik · 2026

LearningCAVs
BibTeX
Download .bib
@article{beerwerth2026zeroshot,
 author = {Beerwerth, Julius and Xu, Jianye and Schäfer, Simon and Belderink, Fynn and Alrifaee, Bassam},
 copyright = {De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act.},
 doi = {10.1515/auto-2025-0057},
 journal = {at - Automatisierungstechnik},
 number = {5},
 pages = {376--385},
 publisher = {De Gruyter},
 title = {Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab},
 volume = {74},
 year = {2026}
}
Heatmaps comparing total reward and control barrier function activation across hyperparameter settings

★Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles

Jianye Xu, Bassam Alrifaee

2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC), in Press · 2026

LearningSafe ControlCAVs
BibTeX
Download .bib
@inproceedings{xu2026safety,
 author = {Xu, Jianye and Alrifaee, Bassam},
 booktitle = {2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC), in Press},
 title = {Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles},
 year = {2026}
}
Collage of presentations and participants at the small-scale testbeds workshop at IEEE IV 2024

★Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap

Jianye Xu, Johannes Betz, Armin Mokhtarian, Archak Mittal, Mengchi Cai, Rahul Mangharam, Omar M. Shehata, Catherine M. Elias, Jan-Nico Zaech, Patrick Scheffe, Felix Jahncke, Sangeet Sankaramangalam Ulhas, Kaj Munhoz Arfvidsson, Bassam Alrifaee

IEEE Intelligent Transportation Systems Magazine · 2026

CAVs
BibTeX
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@article{xu2026smallscale,
 author = {Xu, Jianye and Betz, Johannes and Mokhtarian, Armin and Mittal, Archak and Cai, Mengchi and Mangharam, Rahul and Shehata, Omar M. and Elias, Catherine M. and Zaech, Jan-Nico and Scheffe, Patrick and Jahncke, Felix and Ulhas, Sangeet Sankaramangalam and Arfvidsson, Kaj Munhoz and Alrifaee, Bassam},
 doi = {10.1109/MITS.2026.3666681},
 journal = {IEEE Intelligent Transportation Systems Magazine},
 pages = {2--16},
 title = {Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap},
 year = {2026}
}
About this paper

This paper summarizes the outcomes of the 1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms at IEEE IV 2024. Small-scale testbeds combine rapid prototyping with experiments that approximate real-world conditions more closely than simulations.

High-order control barrier function parameter and trajectory comparisons

★High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation

Jianye Xu, Bassam Alrifaee

arXiv preprint arXiv:2503.15014 · 2025

Earlier preprint; see the updated TTCBF paper (2026).

PreprintSafe Control
BibTeX
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@article{xu2025highorder,
 archiveprefix = {arXiv},
 author = {Xu, Jianye and Alrifaee, Bassam},
 doi = {10.48550/arXiv.2503.15014},
 eprint = {2503.15014},
 journal = {arXiv preprint arXiv:2503.15014},
 primaryclass = {eess},
 publisher = {arXiv},
 title = {High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation},
 year = {2025}
}
Overtaking and bypassing comparisons for collision avoidance

★A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidance

Jianye Xu, Bassam Alrifaee

2025 European Control Conference (ECC) · 2025

LearningSafe ControlCAVs
BibTeX
Download .bib
@inproceedings{xu2025learningbased,
 author = {Xu, Jianye and Alrifaee, Bassam},
 booktitle = {2025 European Control Conference (ECC)},
 doi = {10.23919/ECC65951.2025.11187043},
 pages = {988--995},
 title = {A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidance},
 year = {2025}
}
About this paper

We propose a learning-based CBF to reduce conservatism in collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance between robots' centers as safety margin, neglecting headings and simplifying geometries to circles. While this ensures smooth, differentiable safety functions required by CBFs, it can be overly conservative in tight environments. To address this limitation, we design a heading-aware safety margin that accounts for the robots' orientations, enabling a less conservative and more accurate estimation of safe regions. Since the function computing this safety margin is non-differentiable, we approximate it with a neural network to ensure differentiability and facilitate integration with CBFs. We describe how we achieve bounded learning error and incorporate the upper bound into the CBF to provide formal safety guarantees through forward invariance. We show that our CBF is a high-order CBF with relative degree two for a system with two robots whose dynamics are modeled by the nonlinear kinematic bicycle model. Experimental results in overtaking and bypassing scenarios reveal a 33.5 % reduction in conservatism compared to traditional methods, while maintaining safety

Vehicle footprints in four traffic scenarios with and without the control barrier function safety filter

★A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints

Jianye Xu, Chang Che, Bassam Alrifaee

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC) · 2025

LearningSafe Control
BibTeX
Download .bib
@inproceedings{xu2025realtime,
 author = {Xu, Jianye and Che, Chang and Alrifaee, Bassam},
 booktitle = {2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)},
 doi = {10.1109/ITSC60802.2025.11423203},
 pages = {2818--2825},
 title = {A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints},
 year = {2025}
}
Small-scale vehicle and robot testbeds reviewed in the survey

A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: A Guide for Creating a New Testbed

Armin Mokhtarian, Jianye Xu, Patrick Scheffe, Maximilian Kloock, Simon Schäfer, Heeseung Bang, Viet-Anh Le, Sangeet Ulhas, Johannes Betz, Sean Wilson, Spring Berman, Liam Paull, Amanda Prorok, Bassam Alrifaee

IEEE Robotics & Automation Magazine · 2024

CAVs
BibTeX
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@article{mokhtarian2024survey,
 author = {Mokhtarian, Armin and Xu, Jianye and Scheffe, Patrick and Kloock, Maximilian and Schäfer, Simon and Bang, Heeseung and Le, Viet-Anh and Ulhas, Sangeet and Betz, Johannes and Wilson, Sean and Berman, Spring and Paull, Liam and Prorok, Amanda and Alrifaee, Bassam},
 doi = {10.1109/MRA.2024.3505772},
 journal = {IEEE Robotics & Automation Magazine},
 pages = {2--19},
 title = {A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: A Guide for Creating a New Testbed},
 year = {2024}
}
About this paper

A survey of small-scale testbeds for connected and automated vehicles and robot swarms, with an online comparison of more than 25 existing testbeds.

Course evaluation responses for goals, structure, concept, interest, application, and alignment

Educational Applications of the Cyber-Physical Mobility Lab: A Summary

Simon Schäfer, Jianye Xu, David Klüner, Armin Mokhtarian, Patrick Scheffel, Bassam Alrifaee

2024 European Control Conference (ECC) · 2024

CAVs
BibTeX
Download .bib
@inproceedings{schafer2024educational,
 author = {Schäfer, Simon and Xu, Jianye and Klüner, David and Mokhtarian, Armin and Scheffel, Patrick and Alrifaee, Bassam},
 booktitle = {2024 European Control Conference (ECC)},
 doi = {10.23919/ECC64448.2024.10591176},
 pages = {2666--2671},
 title = {Educational Applications of the Cyber-Physical Mobility Lab: A Summary},
 year = {2024}
}
Colliding and safe intersection trajectories with vehicle predictions and reachable sets

Limiting Computation Levels in Prioritized Trajectory Planning with Safety Guarantees

Patrick Scheffe, Jianye Xu, Bassam Alrifaee

2024 European Control Conference (ECC) · 2024

Safe ControlCAVs
BibTeX
Download .bib
@inproceedings{scheffe2024limiting,
 author = {Scheffe, Patrick and Xu, Jianye and Alrifaee, Bassam},
 booktitle = {2024 European Control Conference (ECC)},
 doi = {10.23919/ECC64448.2024.10591179},
 pages = {297--304},
 title = {Limiting Computation Levels in Prioritized Trajectory Planning with Safety Guarantees},
 year = {2024}
}
Vehicle trajectories in a testbed, intersection, on-ramp, and roundabout

★SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning

Jianye Xu, Pan Hu, Bassam Alrifaee

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) · 2024

LearningCAVs
BibTeX
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@inproceedings{xu2024sigmarl,
 author = {Xu, Jianye and Hu, Pan and Alrifaee, Bassam},
 booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)},
 doi = {10.1109/ITSC58415.2024.10919918},
 pages = {768--775},
 title = {SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning},
 year = {2024}
}
About this paper

Sample efficiency and generalization are two key challenges in MARL. Most RL agents exhibit a limited capacity to generalize, often focusing narrowly on specific scenarios, and are usually evaluated in similar or even the same scenarios seen during training. Various methods have been proposed to address these challenges, including experience replay and regularization. However, how observation design in RL affects sample efficiency and generalization remains an under-explored area. We introduce an open-source, decentralized framework named SigmaRL, designed to enhance both sample efficiency and generalization of MARL for motion planning of CAVs. We address this gap by proposing five strategies to design information-dense observations, focusing on general features that are applicable to most traffic scenarios. We train our RL agents using these strategies on an intersection and evaluate their generalization through numerical experiments across completely unseen traffic scenarios, including a new intersection, an on-ramp, and a roundabout. Incorporating these information-dense observations reduces training times to under one hour on a single CPU, and the evaluation results reveal that our RL agents can effectively zero-shot generalize.

Prioritized agents and their interactions on a road network

★XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity

Jianye Xu, Omar Sobhy, Bassam Alrifaee

arXiv preprint arXiv:2409.11852 · 2024

PreprintLearningCAVs
BibTeX
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@article{xu2024xpmarl,
 archiveprefix = {arXiv},
 author = {Xu, Jianye and Sobhy, Omar and Alrifaee, Bassam},
 doi = {10.48550/arXiv.2409.11852},
 eprint = {2409.11852},
 journal = {arXiv preprint arXiv:2409.11852},
 primaryclass = {cs},
 publisher = {arXiv},
 title = {XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity},
 year = {2024}
}
About this paper

Non-stationarity poses a fundamental challenge in MARL, arising from agents simultaneously learning and altering their policies. This creates a non-stationary environment from the perspective of each individual agent, often leading to suboptimal or even unconverged learning outcomes. We propose an open-source framework named XP-MARL, which augments MARL with auxiliary prioritization to address this challenge in cooperative settings. XP-MARL is 1) founded upon our hypothesis that prioritizing agents and letting higher-priority agents establish their actions first would stabilize the learning process and thus mitigate non-stationarity and 2) enabled by our proposed mechanism called action propagation, where higher-priority agents act first and communicate their actions, providing a more stationary environment for others. Moreover, instead of using a predefined or heuristic priority assignment, XP-MARL learns priority-assignment policies with an auxiliary MARL problem, leading to a joint learning scheme. Experiments in a motion-planning scenario involving CAVs demonstrate that XP-MARL improves the safety of a baseline model by 84.4% and outperforms a state-of-the-art approach, which improves the baseline by only 12.8%

Education

Awards

Professional Activities

Program Committee

  • Associate Editor

    · IEEE International Conference on Intelligent Transportation Systems (ITSC) · Serves as an Associate Editor for IEEE ITSC 2026.
  • Invited Session Organizer

    · IEEE ITSC · Naples, Italy · Co-organized the invited session “Bridging Optimal Control and Learning for Connected and Automated Vehicles”.
  • Workshop Co-Organizer

    · IEEE ITSC · Gold Coast, Australia · Co-organized the workshop “Automated Mobility in Mixed Traffic—Emerging Mixed Autonomy in the Era of AI”.
  • Workshop Co-Organizer

    · IEEE IV · Jeju, South Korea · Co-organized the “1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms”.
  • Competition Co-Organizer

    · CPM Olympics · Aachen, Germany · Co-organized CPM Olympics, a motion planning competition for connected and automated vehicles.

Invited Talks

  • Workshop Invited Speaker

    · IEEE ITSC · Naples, Italy · Invited to speak at the workshop “The 16th Workshop & Industry Panel on Cooperative and Automated Driving and Future Mobility”.
  • Workshop Invited Speaker

    · IEEE ITSC · Gold Coast, Australia · Invited to speak at the workshop “Automated Mobility in Mixed Traffic—Emerging Mixed Autonomy in the Era of AI”.

Journal Reviewing

Automatica, IEEE Control Systems Letters (L-CSS), IEEE Transactions on Intelligent Transportation Systems (T-ITS), IEEE Intelligent Transportation Systems Magazine (ITSM), IEEE Robotics and Automation Letters (RA-L).

Conference Reviewing

IEEE International Conference on Intelligent Transportation Systems (ITSC), IEEE Intelligent Vehicles Symposium (IV), European Control Conference (ECC), IEEE International Conference on Robotics and Automation (ICRA).

Membership

IEEE Graduate Student Member, IEEE Intelligent Transportation Systems Society, IEEE Robotics and Automation Society, IEEE Control Systems Society, IEEE Young Professionals.

Teaching and Mentoring

  • Assistant Lecturer

    RWTH Aachen University

    Served as an assistant lecturer for the master’s course “Control and Perception in Networked and Autonomous Vehicles”.

  • Guest Lecturer

    RWTH Aachen University, Germany

    Gave a guest lecture titled “Control Barrier Functions and Their Applications to Collision Avoidance in Networked and Autonomous Vehicles” for a master’s course.

  • Research Assistant

    Institute for Plastics Processing (IKV), RWTH Aachen University, Germany

    Developed a web-based interface for injection molding process optimization using JavaScript and Vue 3. Used Python for data processing and analysis with pandas and Keras.

  • Undergraduate Teaching Assistant

    Beijing Institute of Technology

    Assisted with CAD software training lectures for first-year students.

Supervised theses (20)
  • 2026 Ali Elbadawy · Integrating Barrier Functions into Multi-Agent Social Value Orientation for Unsignalized Intersections

    Master's thesis, RWTH Aachen University

  • 2026 Massimo Marcon · Latent World Models for Motion Planning of Connected and Automated Vehicles

    Master's thesis, RWTH Aachen University

  • 2026 Jiaxin Liu · Mixture of Expert Policies for Decision-Making and Motion Planning in Connected and Automated Vehicles

    Master's thesis, RWTH Aachen University

  • 2026 George Halim · Barrier-Certified Exploration for Real-World Reinforcement Learning in the Cyber-Physical Mobility Lab

    Bachelor's thesis, RWTH Aachen University

  • 2026 Yousef Sayed · A Study of Responsibility Assignment in Control Barrier Function for Connected and Automated Vehicles

    Bachelor's thesis, RWTH Aachen University

  • 2025 Maximilian Petri · Reinforcement Learning for Formula Student Autonomous Racing

    Master's thesis, RWTH Aachen University

  • 2025 Chang Che · Constraint-Enforced Multi-Agent Reinforcement Learning for Safe Motion Planning of Connected and Automated Vehicles

    Master's thesis, RWTH Aachen University

  • 2025 Pan Hu · Situation-Aware Trajectory Planning of Connected and Automated Vehicles with Multi-Policy Reinforcement Learning

    Master's thesis, RWTH Aachen University

  • 2025 Daniel Boules · A Prioritization-Based Approach for Enhanced Stability in Non-Stationary Multi-Agent Reinforcement Learning

    Bachelor's thesis, RWTH Aachen University

  • 2025 Fynn Belderink · Model-Based Receding-Horizon Planning in Multi-Agent Reinforcement Learning

    Bachelor's thesis, RWTH Aachen University

  • 2025 Yunjia Chen · Sensor Fusion-Enhanced Map Matching of Bicycle Routes Using Crowdsourced GPS and IMU Data from Smartphones

    Bachelor's thesis, RWTH Aachen University

  • 2024 Xinyue Cui · Determining the Longitudinal and Lateral Acceleration of a Bicycle Using a Smartphone Mounted on Its Handlebar

    Master's thesis, RWTH Aachen University

  • 2024 Lebo Liang · Safe Multi-Agent Control: Integrating Control Barrier Functions with Reinforcement Learning

    Bachelor's thesis, Aachen University of Applied Sciences

  • 2024 Mohamed Shamekh · Model-Predictive-Control-Based Safe Multi-Agent Reinforcement Learning for Motion Planning

    Bachelor's thesis, RWTH Aachen University

  • 2024 Omar Sobhy · Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning Through Learning-Based Dynamic Prioritization

    Bachelor's thesis, RWTH Aachen University

  • 2024 Xia Yan · Learning-Based Motion Planner for Connected and Automated Vehicles

    Master's thesis, RWTH Aachen University

  • 2024 Xilei Chen · Imitation Learning from Optimization-Based Motion Planners for Connected and Automated Vehicles

    Bachelor's thesis, RWTH Aachen University

  • 2024 Raphael Malig · Investigating the Effect of Discretizing Continuous Action Space in Multi-Agent Reinforcement Learning for Motion Planning

    Bachelor's thesis, RWTH Aachen University

  • 2024 Andreas Pletschko · Leveraging Temporal Dependencies for Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning in Motion Planning

    Bachelor's thesis, RWTH Aachen University

  • 2023 Ruizhang Zhou · AI-Based Generation of Testing Scenarios for Motion Planners on Connected and Automated Vehicles

    Master's thesis, RWTH Aachen University

Supervised seminar works (14)

Summer Term 2026

Lisa Michèle Schilling. A Survey of Large Language Models for Autonomous Driving (RWTH Aachen University).

Philipp Krütt. Policy Learning and Dataset Generation for Cooperative Driving of Connected and Automated Vehicles at a Roundabout (RWTH Aachen University).

Winter Term 2025/2026

Alexander Lingmann. Generating Testing Scenarios for Motion Planning of Connected and Automated Vehicles (RWTH Aachen University).

Clemens von Molo. Incorporating Different High-Definition Maps in SigmaRL for Connected and Automated Vehicles (RWTH Aachen University).

Summer Term 2025

Mats Ploder. Safe Reinforcement Learning with Control Barrier Functions for Autonomous Driving: A Survey (RWTH Aachen University).

Burak Kayabasi. Straßengrenzen bei der Verwendung von Control Barrier Functions für autonomes Fahren: Ein Überblick (RWTH Aachen University).

Winter Term 2024/2025

Hannes Tettenborn. Survey of Attention Mechanisms in Reinforcement Learning for Motion Planning in Autonomous Driving (RWTH Aachen University).

Julian Büttner. Generating Testing Scenarios for Motion Planning of Connected and Automated Vehicles (RWTH Aachen University).

Lasse Jacob. Implementation of an Image-Based Observation Representation in SigmaRL (RWTH Aachen University).

Summer Term 2024

Colin Kubon. Investigating the Feasibility of Integrating Lanelet2 into CPM Lab (RWTH Aachen University).

Destay Desin Sahan. A Survey of Safe Reinforcement Learning for Autonomous Vehicles (RWTH Aachen University).

Winter Term 2023/2024

Jonathan Kock. Implementation of a Multi-Agent Imitation Learning Framework for Connected and Automated Vehicles (RWTH Aachen University).

Nico Nußbaum. Implementation of a Multi-Agent Deep Reinforcement Learning Framework for Connected and Automated Vehicles (RWTH Aachen University).

Zirui Bai. Multi-Agent Deep Reinforcement Learning for Connected and Automated Vehicles: A Review (RWTH Aachen University).