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.

Subject headings

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)

Keyword

Roads

Trajectory

Safety

Autonomous vehicles

uncertain constraints

Predictive models

Behavioral sciences

recursive feasibility

nonlinear predictive control

safety

Autonomous driving

Planning

Publication and Content Type

art (subject category)

ref (subject category)