2 citations · 5 across the 12 of their papers we have counts for
12 papers
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…
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…
Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab
Julius Beerwerth, Jianye Xu, Simon Schäfer +2
We present a reproducible benchmark for evaluating sim-to-real transfer of Multi-Agent Reinforcement Learning (MARL) policies for Connected and Automated Vehicles (CAVs). The platf…
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,…
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…
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…
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…