Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario

Hexuan Li; Francesco De Cristofaro; Faris Orucevic; Zhengguo GU; Arno Eichberger

doi:10.1016/j.trpro.2023.11.897

Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario

Hexuan Li, Francesco De Cristofaro, Faris Orucevic, Zhengguo GU, Arno Eichberger

Institute of Automotive Engineering (3310)

Research output: Contribution to journal › Article › peer-review

Abstract

The increasing demand for reliable and safe high-driving automation algorithms in Autonomous Driving (AD) vehicles has driven significant advancements in the industry. This paper investigates the performance of a specific AD vehicle architecture in a critical lane change maneuver, extracted from the HighD Dataset, which consists of naturalistic vehicle trajectories recorded on German highways. The analysis focuses on different parameter configurations to understand their influence on the results. The selected AD algorithm is based on the Apollo Open Autonomous Driving Platform (AOADP). Simulation results demonstrate the significant impact of path planning algorithm parameters on lane change execution. The comparison between human driving behavior and AD systems plays a crucial role in determining sensing technology specifications. This approach provides a quantitative analysis of the impact on autonomous driving measurements, contributing to the safe specification of AD functions. These findings lay the foundation for future evaluations and improvements in the selected AD architecture.

Original language	English
Pages (from-to)	102-109
Journal	Transportation Research Procedia
Volume	2023
Issue number	73
DOIs	https://doi.org/10.1016/j.trpro.2023.11.897
Publication status	Published - 27 Dec 2023

Keywords

Apollo Autonomous Driving Platform
Lane change path planning
Simulation

ASJC Scopus subject areas

Automotive Engineering

Fields of Expertise

Mobility & Production

Treatment code (Nähere Zuordnung)

Theoretical

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.trpro.2023.11.897Licence: CC BY-NC-ND 4.0

Cite this

@article{c64aa9e6edac4447b1f90b9dd3b0bd2d,

title = "Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario",

abstract = "The increasing demand for reliable and safe high-driving automation algorithms in Autonomous Driving (AD) vehicles has driven significant advancements in the industry. This paper investigates the performance of a specific AD vehicle architecture in a critical lane change maneuver, extracted from the HighD Dataset, which consists of naturalistic vehicle trajectories recorded on German highways. The analysis focuses on different parameter configurations to understand their influence on the results. The selected AD algorithm is based on the Apollo Open Autonomous Driving Platform (AOADP). Simulation results demonstrate the significant impact of path planning algorithm parameters on lane change execution. The comparison between human driving behavior and AD systems plays a crucial role in determining sensing technology specifications. This approach provides a quantitative analysis of the impact on autonomous driving measurements, contributing to the safe specification of AD functions. These findings lay the foundation for future evaluations and improvements in the selected AD architecture. ",

keywords = "Apollo Autonomous Driving Platform, Lane change path planning, Simulation, Automated Driving, Virtual Simulation, Trajectory Planning, ADAS Evaluation",

author = "Hexuan Li and {De Cristofaro}, Francesco and Faris Orucevic and Zhengguo GU and Arno Eichberger",

year = "2023",

month = dec,

day = "27",

doi = "10.1016/j.trpro.2023.11.897",

language = "English",

volume = "2023",

pages = "102--109",

journal = "Transportation Research Procedia",

issn = "2352-1465",

publisher = "Elsevier B.V.",

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TY - JOUR

T1 - Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario

AU - Li, Hexuan

AU - De Cristofaro, Francesco

AU - Orucevic, Faris

AU - GU, Zhengguo

AU - Eichberger, Arno

PY - 2023/12/27

Y1 - 2023/12/27

N2 - The increasing demand for reliable and safe high-driving automation algorithms in Autonomous Driving (AD) vehicles has driven significant advancements in the industry. This paper investigates the performance of a specific AD vehicle architecture in a critical lane change maneuver, extracted from the HighD Dataset, which consists of naturalistic vehicle trajectories recorded on German highways. The analysis focuses on different parameter configurations to understand their influence on the results. The selected AD algorithm is based on the Apollo Open Autonomous Driving Platform (AOADP). Simulation results demonstrate the significant impact of path planning algorithm parameters on lane change execution. The comparison between human driving behavior and AD systems plays a crucial role in determining sensing technology specifications. This approach provides a quantitative analysis of the impact on autonomous driving measurements, contributing to the safe specification of AD functions. These findings lay the foundation for future evaluations and improvements in the selected AD architecture.

AB - The increasing demand for reliable and safe high-driving automation algorithms in Autonomous Driving (AD) vehicles has driven significant advancements in the industry. This paper investigates the performance of a specific AD vehicle architecture in a critical lane change maneuver, extracted from the HighD Dataset, which consists of naturalistic vehicle trajectories recorded on German highways. The analysis focuses on different parameter configurations to understand their influence on the results. The selected AD algorithm is based on the Apollo Open Autonomous Driving Platform (AOADP). Simulation results demonstrate the significant impact of path planning algorithm parameters on lane change execution. The comparison between human driving behavior and AD systems plays a crucial role in determining sensing technology specifications. This approach provides a quantitative analysis of the impact on autonomous driving measurements, contributing to the safe specification of AD functions. These findings lay the foundation for future evaluations and improvements in the selected AD architecture.

KW - Apollo Autonomous Driving Platform

KW - Lane change path planning

KW - Simulation

KW - Automated Driving

KW - Virtual Simulation

KW - Trajectory Planning

KW - ADAS Evaluation

UR - https://www.sciencedirect.com/science/article/pii/S2352146523011961

U2 - 10.1016/j.trpro.2023.11.897

DO - 10.1016/j.trpro.2023.11.897

M3 - Article

SN - 2352-1465

VL - 2023

SP - 102

EP - 109

JO - Transportation Research Procedia

JF - Transportation Research Procedia

IS - 73

ER -

Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario

Abstract

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

Other files and links

Fingerprint

21_FFG_InVADE - Integrated Vehicle-in-the-Loop for Automated Driving and E-mobility

DVS: Vehicle Dynamics

Cite this

Quantitative Analysis of the Impact of Baidu Apollo Parameterization on Trajectory Planning in a Critical Scenario

Abstract

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

21_FFG_InVADE - Integrated Vehicle-in-the-Loop for Automated Driving and E-mobility

DVS: Vehicle Dynamics

Cite this