Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments

• The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle can be operated in a provably safe manner, i.e., the vehicle must be able to avoid collisions in any possible traffic situation. In this article, we propose a framework based on model predictive control (MPC) that endows the self-driving vehicle with the necessary safety guarantees. In particular, our framework ensures constraint satisfaction at all times while tracking the reference trajectory as close as obstacles allow, resulting in a safe and comfortable driving behavior. To discuss the performance and real-time capability of our framework, we provide first an illustrative simulation example, and then, we demonstrate the effectiveness of our framework in experiments with a real test vehicle.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Roads

Trajectory

Safety

Autonomous vehicles

uncertain constraints

Predictive models

Behavioral sciences

recursive feasibility

nonlinear predictive control

safety

Autonomous driving

Planning

Publikations- och innehållstyp

art (ämneskategori)

ref (ämneskategori)