Abstract
Autonomous driving is a consistent new trend when discussing future smart cities. Safety guarantees are a crucial concern in the design of vehicle navigation systems operating on heterogeneous environments such as streets and roads, in order to prevent severe harm to passengers and damage to expensive equipment. This chapter proposes a path following control method for vehicle navigation in dynamic and fast response situations such as autonomous driving in a multi-lane highway, guaranteeing collision avoidance between pairs of vehicles and between each vehicle and the lane limits. A Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for achieving the aforementioned objective is proposed and implemented by means of a Quadratic Programming (QP)-based controller. Convex barrier functions are proposed for modeling vehicle collisions and B-Splines are used both as paths to be followed and as models for the lane limits. Simulation results using a Python-based framework for vehicle navigation are presented and discussed, demonstrating the viability of the proposed framework for autonomous driving.
This work was supported in part by Fundação para a Ciência e Tecnologia (FCT), Portugal, Ph.D. Grant 2020.06795.BD and the Project RELIABLE (PTDC/EEI-AUT/3522/2020), the Associated Laboratory ARISE (LA/P/0112/2020), the R &D Unit SYSTEC (Base UIDB/00147/2020 and Programmatic UIDP/00147/2020 funds), both funded by national funds through the FCT/MCTES (PIDDAC).
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Reis, M.F., Andrade, G.A., Aguiar, A.P. (2024). Safe Autonomous Multi-vehicle Navigation Using Path Following Control and Spline-Based Barrier Functions. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-031-58676-7_24
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