2 citations · 7 across the 13 of their papers we have counts for
14 papers
Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning
Dvij Kalaria, Qin Lin, John M. Dolan
Reinforcement learning (RL) agents need to explore their environment to learn optimal behaviors and achieve maximum rewards. However, exploration can be risky when training RL dire…
Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement
Jinfeng Chen, Zhiqiang Gao, Qin Lin
Control barrier functions-based quadratic programming (CBF-QP) is gaining popularity as an effective controller synthesis tool for safe control. However, the provable safety is est…
Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning
Dvij Kalaria, Qin Lin, John M. Dolan
Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times while also activel…
Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine
Dvij Kalaria, Qin Lin, John M. Dolan
Autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times. Autonomous race cars require h…
Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racing
Dvij Kalaria, Qin Lin, John M. Dolan
Autonomous car racing is a challenging task, as it requires precise applications of control while the vehicle is operating at cornering speeds. Traditional autonomous pipelines req…
Spatio-temporal Motion Planning for Autonomous Vehicles with Trapezoidal Prism Corridors and Bézier Curves
Srujan Deolasee, Qin Lin, Jialun Li +1
Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free trajectories. A layered motion planning approach with decoupled path and speed planni…
Delay-aware Robust Control for Safe Autonomous Driving and Racing
Dvij Kalaria, Qin Lin, John M. Dolan
Delays endanger safety of autonomous systems operating in a rapidly changing environment, such as nondeterministic surrounding traffic participants in autonomous driving and high-s…
Delay-aware Robust Control for Safe Autonomous Driving
Dvij Kalaria, Qin Lin, John M. Dolan
With the advancement of affordable self-driving vehicles using complicated nonlinear optimization but limited computation resources, computation time becomes a matter of concern. O…
Safe planning and control under uncertainty for self-driving
Shivesh Khaitan, Qin Lin, John M. Dolan
Motion Planning under uncertainty is critical for safe self-driving. In this paper, we propose a unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle…
Safe Planning for Self-Driving Via Adaptive Constrained ILQR
Yanjun Pan, Qin Lin, Het Shah +1
Constrained Iterative Linear Quadratic Regulator (CILQR), a variant of ILQR, has been recently proposed for motion planning problems of autonomous vehicles to deal with constraints…
Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data
Qin Lin, Sicco Verwer, John Dolan
Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data. For safety-critical systems such as autonomous vehicles, it can be…
Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
Qin Lin, Wenshuo Wang, Yihuan Zhang +1
Understanding multi-vehicle interactive behaviors with temporal sequential observations is crucial for autonomous vehicles to make appropriate decisions in an uncertain traffic env…
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
Xiaoran Liu, Qin Lin, Sicco Verwer +1
This paper focuses on detecting anomalies in a digital video broadcasting (DVB) system from providers' perspective. We learn a probabilistic deterministic real timed automaton prof…
Interpreting Finite Automata for Sequential Data
Christian Albert Hammerschmidt, Sicco Verwer, Qin Lin +1
Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specif…