SLAS: Speed and Lane Advisory System for Highway Navigation

Abstract

This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules. Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic. We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort. We further incorporate various modifications in the proposed approach to improve the overall computational efficiency and achieve real-time performance. We demonstrate the efficacy of the proposed approach in contrast to the existing methods, when applied in conjunction with state-of-the-art trajectory generation and trajectory following frameworks, in a CARLA simulation environment.

CommonRoad-CARLA Interface: Bridging the Gap between Motion Planning and 3D Simulation

“…For safe motion planning, CARLA has the Responsibility Safety Standard (RSS) [28] [29] integrated. Several authors use CARLA to show the usability of their motion planning approach in realistic environments, e.g., to evaluate traffic rule compliance [30]- [33], reinforcement learning [34], highway planning [35], and planning with a focus on critical situations [36]. To interact with CARLA, one needs to develop custom interfaces, which limits the exchange and comparison of research results and requires much work by researchers and developers.…”