Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation
The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety constraints are not scalable and require a long NMPC horizon. Moreover, the adoption of NMPC in the automotive industry is limited by the heavy computation of numerical optimization routines. To address those issues, this paper presents a real-time capable NMPC for automated driving in urban environments, using control barrier functions (CBFs). Furthermore, the designed NMPC is based on a novel collocation transcription approach, named RESAFE/COL, that allows to reduce the number of optimization variables while still guaranteeing the continuous time (nonlinear) inequality constraints satisfaction, through regional convex hull approximation. RESAFE/COL is proven to be 5 times faster than multiple shooting and more tractable for embedded hardware without a decrease in the performance, nor accuracy and safety of the numerical solution. We validate our NMPC-CBF with RESAFE/COL on digital twins of the vehicle and the urban environment and show the safe controller's ability to improve crash avoidance by 91\%. Supplementary visual material can be found at https://youtu.be/_EnbfYwljp4.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingModel Predictive ControlSimilar Papers 제목 키워드 기반
Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions
Guaranteeing safety for robotic and autonomous systems in real-world environments is a challenging task that requires the mitigation of stochastic uncertainties. Control barrier functions have, in recent years, been wide…
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions
Ensuring safety in autonomous systems requires controllers that aim to satisfy state-wise constraints without relying on online interaction.While existing Safe Offline RL methods typically enforce soft expected-cost cons…
Offline RLSafe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control…
Autonomous DrivingImitation Learningreinforcement-learningReinforcement Learning+2Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving
Guaranteeing safety of perception-based learning systems is challenging due to the absence of ground-truth state information unlike in state-aware control scenarios. In this paper, we introduce a safety guaranteed learni…
Autonomous DrivingSafe Autonomous Docking Maneuvers for a Floating Platform based on Input Sharing Control Barrier Functions
In this article, we present a control strategy for the problem of safe autonomous docking for a planar floating platform (Slider) that emulates the movement of a satellite. Employing the proposed strategy, Slider approac…