Getting SMARTER for Motion Planning in Autonomous Driving Systems

TL;DR

This paper introduces SMARTS 2.0, an advanced, highly customizable simulation platform for autonomous vehicle motion planning, along with a comprehensive benchmark suite for evaluating algorithms in complex, realistic scenarios.

Contribution

The paper presents SMARTS 2.0, a new simulation environment with enhanced features and a novel benchmark suite for rigorous evaluation of motion planning algorithms in autonomous driving.

Findings

Evaluation of common algorithms reveals significant challenges in complex scenarios.

The benchmark highlights the need for more robust planning algorithms.

SMARTS 2.0 facilitates large-scale, realistic simulation for autonomous driving research.

Abstract

Motion planning is a fundamental problem in autonomous driving and perhaps the most challenging to comprehensively evaluate because of the associated risks and expenses of real-world deployment. Therefore, simulations play an important role in efficient development of planning algorithms. To be effective, simulations must be accurate and realistic, both in terms of dynamics and behavior modeling, and also highly customizable in order to accommodate a broad spectrum of research frameworks. In this paper, we introduce SMARTS 2.0, the second generation of our motion planning simulator which, in addition to being highly optimized for large-scale simulation, provides many new features, such as realistic map integration, vehicle-to-vehicle (V2V) communication, traffic and pedestrian simulation, and a broad variety of sensor models. Moreover, we present a novel benchmark suite for evaluating planning algorithms in various highly challenging scenarios, including interactive driving, such as turning at intersections, and adaptive driving, in which the task is to closely follow a lead vehicle without any explicit knowledge of its intention. Each scenario is characterized by a variety of traffic patterns and road structures. We further propose a series of common and task-specific metrics to effectively evaluate the performance of the planning algorithms. At the end, we evaluate common motion planning algorithms using the proposed benchmark and highlight the challenges the proposed scenarios impose. The new SMARTS 2.0 features and the benchmark are publicly available at github.com/huawei-noah/SMARTS.