Trajectory Planning for Automated Driving in Intersection Scenarios using Driver Models

TL;DR

This paper introduces a novel trajectory planning framework for autonomous vehicles at intersections that balances social compliance, comfort, and efficiency by integrating driver models with local optimization.

Contribution

It presents a new framework combining driver models and local optimization to improve trajectory planning for urban intersections, emphasizing social behavior and comfort.

Findings

Framework ensures social compliance and comfort.

Demonstrates fast behavior prediction and maneuver generation.

Effective in various intersection scenarios.

Abstract

Efficient trajectory planning for urban intersections is currently one of the most challenging tasks for an Autonomous Vehicle (AV). Courteous behavior towards other traffic participants, the AV's comfort and its progression in the environment are the key aspects that determine the performance of trajectory planning algorithms. To capture these aspects, we propose a novel trajectory planning framework that ensures social compliance and simultaneously optimizes the AV's comfort subject to kinematic constraints. The framework combines a local continuous optimization approach and an efficient driver model to ensure fast behavior prediction, maneuver generation and decision making over long horizons. The proposed framework is evaluated in different scenarios to demonstrate its capabilities in terms of the resulting trajectories and runtime.