A Framework for Multi-Scenario Performance Evaluation of Modular Autonomous Driving Systems Wuhan University of Technology

Format:

Book

Conference/Event

Author/Creator:

Jia, Chunyu, author.

Contributor:

Kong, Yan

Ma, Yao

Pei, Xiaofei

Conference Name:

SAE 2025 Intelligent and Connected Vehicles Symposium (2025-09-19 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Currently, we face the challenge that ensuring ADS safety remains the primary bottleneck to large-scale commercial deploymentwhile benchmarks such as the CARLA Leaderboard have spurred progress, their coarse evaluation granularity, inability to quantify procedural risks, and lack of differentiation among algorithms in complex scenarios make in-depth diagnostics and functional safety validation exceedingly difficult. To address these challenges, we propose EvalDrive, a framework that seems to offer a more comprehensive approach to multi-scenario performance evaluation for modular autonomous driving systems. Within this broader analytical framework, EvalDrive appears to provide what seems to be three key contributions. (1) It constructs what appears to represent a structured and extensible scenario library, comprising a majority of 44 interactive scenarios, 23 weather conditions, and 12 town environments, which are then systematically expanded through parameterized variations. (2) Our paper presents a multi-dimensional evaluation approach that shifts the emphasis from outcome-based safety to process-oriented safety, enabling the quantification of near-collision behaviors. Moreover, context-aware metricssuch as the Index of Driving Efficiency (IDE)are employed to characterize distinct driving styles. (3) The framework further implements a highly integrated closed-loop co-simulation platform. By tightly coupling the CARLA simulator with the Apollo ADS, it establishes a high-fidelity, reproducible software-in-the-loop (SIL) environment. What this pattern seems to suggest, therefore, is that EvalDrive provides what appears to be a more comprehensive paradigmfrom scenario construction and multi-dimensional evaluation to closed-loop validationseeming to offer more robust diagnostic support for the iterative optimization and safe deployment of autonomous driving systems

Notes:

Vendor supplied data

Publisher Number:

2025-01-7319

Access Restriction:

Restricted for use by site license