22 citations · 49 across the 25 of their papers we have counts for
27 papers
Safeguarding Multi-Agent Reinforcement Learning for Connected and Automated Vehicles with Recursively Feasible Control Barrier Functions
Jianye Xu, Yousef Sayed, Bassam Alrifaee
Multi-Agent Reinforcement Learning (MARL) shows promise in coordinating connected and automated vehicles but offers no safety guarantee. Although Control Barrier Functions (CBFs) c…
Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles
Jianye Xu, Bassam Alrifaee
Reinforcement Learning (RL) uses rewards to guide learning, yet reward design is typically hand-crafted using heuristics that can be difficult to tune. We propose a Control Barrier…
Ufil: A Unified Framework for Infrastructure-based Localization
Simon Schäfer, Lucas Hegerath, Marius Molz +2
Infrastructure-based localization enhances road safety and traffic management by providing state estimates of road users. Development is hindered by fragmented, application-specifi…
TTCBF: A Truncated Taylor Control Barrier Function for High-Order Safety Constraints
Jianye Xu, Bassam Alrifaee
Control Barrier Functions (CBFs) enforce safety by rendering a prescribed safe set forward invariant. However, standard CBFs are limited to safety constraints with relative degree…
Lidar-based Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure
Simon Schäfer, Bassam Alrifaee, Ehsan Hashemi
This paper presents a lidar-only state estimation and tracking framework, along with a roadside sensing unit for integration with existing urban infrastructure. Urban deployments d…
Integrated Wheel Sensor Communication using ESP32 -- A Contribution towards a Digital Twin of the Road System
Ventseslav Yordanov, Simon Schäfer, Alexander Mann +3
While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the interaction between tires and ro…
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang +12
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing hav…
Simulation to Reality: Testbeds and Architectures for Connected and Automated Vehicles
David Klüner, Simon Schäfer, Lucas Hegerath +5
Ensuring the safe and efficient operation of CAVs relies heavily on the software framework used. A software framework needs to ensure real-time properties, reliable communication,…
Automotive Middleware Performance: Comparison of FastDDS, Zenoh and vSomeIP
David Philipp Klüner, Lucas Hegerath, Amin Dieter Hatib +3
In this study, we evaluate the performance of current automotive communication middlewares under various operating conditions. Specifically, we examine FastDDS, a widely used open-…
A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints
Jianye Xu, Chang Che, Bassam Alrifaee
We present a real-time safety filter for motion planning, including those that are learning-based, using Control Barrier Functions (CBFs) to provide formal guarantees for collision…
High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation
Jianye Xu, Bassam Alrifaee
We examine the complexity of the standard High-Order Control Barrier Function (HOCBF) approach and propose a truncated Taylor-based approach that reduces design parameters. First,…
Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap
Jianye Xu, Johannes Betz, Armin Mokhtarian +11
This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effor…
Graph Coloring to Reduce Computation Time in Prioritized Planning
Patrick Scheffe, Julius Kahle, Bassam Alrifaee
Distributing computations among agents in large networks reduces computational effort in multi-agent path finding (MAPF). One distribution strategy is prioritized planning (PP). In…
Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning
Patrick Scheffe, Julius Kahle, Bassam Alrifaee
Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to…
Modern Middlewares for Automated Vehicles: A Tutorial
David Philipp Klüner, Marius Molz, Alexandru Kampmann +2
This paper offers a tutorial on current middlewares in automated vehicles. Our aim is to provide the reader with an overview of current middlewares and to identify open challenges…
A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidance
Jianye Xu, Bassam Alrifaee
We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance for car-like robots. Traditional CBFs often use the Euclidean distance betw…
XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity
Jianye Xu, Omar Sobhy, Bassam Alrifaee
Non-stationarity poses a fundamental challenge in Multi-Agent Reinforcement Learning (MARL), arising from agents simultaneously learning and altering their policies. This creates a…
Limiting Computation Levels in Prioritized Trajectory Planning with Safety Guarantees
Jianye Xu, Patrick Scheffe, Bassam Alrifaee
In prioritized planning for vehicles, vehicles plan trajectories in parallel or in sequence. Parallel prioritized planning offers approximately consistent computation time regardle…
Synchronization-Based Cooperative Distributed Model Predictive Control
Julius Beerwerth, Maximilian Kloock, Bassam Alrifaee
Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading t…
A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms
Armin Mokhtarian, Jianye Xu, Patrick Scheffe +11
Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors…
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning
Jianye Xu, Pan Hu, Bassam Alrifaee
This paper introduces an open-source, decentralized framework named SigmaRL, designed to enhance both sample efficiency and generalization of multi-agent Reinforcement Learning (RL…
Optimization-based motion primitive automata for autonomous driving
Matheus V. A. Pedrosa, Patrick Scheffe, Bassam Alrifaee +1
Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on opti…
Investigating Outdoor Recognition Performance of Infrared Beacons for Infrastructure-based Localization
Alexandru Kampmann, Michael Lamberti, Nikola Petrovic +2
This paper demonstrates a system comprised of infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacon…
Reducing Uncertainty by Fusing Dynamic Occupancy Grid Maps in a Cloud-based Collective Environment Model
Bastian Lampe, Raphael van Kempen, Timo Woopen +3
Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple…