A Model-Predictive Motion Planner for the IARA Autonomous Car

TL;DR

This paper introduces a fast, real-time model-predictive motion planner for the IARA autonomous car that generates smooth, safe trajectories closely following human-driven paths at speeds up to 32.4 km/h.

Contribution

The paper presents a novel MPMP algorithm capable of computing smooth, safe trajectories in under 50 ms for autonomous driving applications.

Findings

Trajectory following accuracy of 0.15 m on average

Smooth driving at speeds up to 32.4 km/h

Real-time trajectory computation within 50 ms

Abstract

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of eventual obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).