What Truly Matters in Trajectory Prediction for Autonomous Driving?

Bibliographic Details
Title:	What Truly Matters in Trajectory Prediction for Autonomous Driving?
Authors:	Tran, Phong, Wu, Haoran, Yu, Cunjun, Cai, Panpan, Zheng, Sifa, Hsu, David
Publication Year:	2023
Collection:	Computer Science
Subject Terms:	Computer Science - Robotics, Computer Science - Artificial Intelligence
More Details:	Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement error (FDE), is widely used as a performance metric. However, a significant disparity exists between the accuracy of predictors on fixed datasets and driving performance when the predictors are used downstream for vehicle control, because of a dynamics gap. In the real world, the prediction algorithm influences the behavior of the ego vehicle, which, in turn, influences the behaviors of other vehicles nearby. This interaction results in predictor-specific dynamics that directly impacts prediction results. In fixed datasets, since other vehicles' responses are predetermined, this interaction effect is lost, leading to a significant dynamics gap. This paper studies the overlooked significance of this dynamics gap. We also examine several other factors contributing to the disparity between prediction performance and driving performance. The findings highlight the trade-off between the predictor's computational efficiency and prediction accuracy in determining real-world driving performance. In summary, an interactive, task-driven evaluation protocol for trajectory prediction is crucial to capture its effectiveness for autonomous driving. Source code along with experimental settings is available online. Comment: NeurIPS 2023
Document Type:	Working Paper
Access URL:	http://arxiv.org/abs/2306.15136
Accession Number:	edsarx.2306.15136
Database:	arXiv

FullText	Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2306.15136 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20230626&spage=&pages=&title=What Truly Matters in Trajectory Prediction for Autonomous Driving?&atitle=What%20Truly%20Matters%20in%20Trajectory%20Prediction%20for%20Autonomous%20Driving%3F&aulast=Tran%2C%20Phong&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries
Header	DbId: edsarx DbLabel: arXiv An: edsarx.2306.15136 RelevancyScore: 1057 AccessLevel: 3 PubType: Report PubTypeId: report PreciseRelevancyScore: 1057.34545898438
IllustrationInfo
Items	– Name: Title Label: Title Group: Ti Data: What Truly Matters in Trajectory Prediction for Autonomous Driving? – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tran%2C+Phong%22">Tran, Phong</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Haoran%22">Wu, Haoran</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Cunjun%22">Yu, Cunjun</searchLink><br /><searchLink fieldCode="AR" term="%22Cai%2C+Panpan%22">Cai, Panpan</searchLink><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Sifa%22">Zheng, Sifa</searchLink><br /><searchLink fieldCode="AR" term="%22Hsu%2C+David%22">Hsu, David</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+-+Robotics%22">Computer Science - Robotics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Artificial+Intelligence%22">Computer Science - Artificial Intelligence</searchLink> – Name: Abstract Label: Description Group: Ab Data: Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement error (FDE), is widely used as a performance metric. However, a significant disparity exists between the accuracy of predictors on fixed datasets and driving performance when the predictors are used downstream for vehicle control, because of a dynamics gap. In the real world, the prediction algorithm influences the behavior of the ego vehicle, which, in turn, influences the behaviors of other vehicles nearby. This interaction results in predictor-specific dynamics that directly impacts prediction results. In fixed datasets, since other vehicles' responses are predetermined, this interaction effect is lost, leading to a significant dynamics gap. This paper studies the overlooked significance of this dynamics gap. We also examine several other factors contributing to the disparity between prediction performance and driving performance. The findings highlight the trade-off between the predictor's computational efficiency and prediction accuracy in determining real-world driving performance. In summary, an interactive, task-driven evaluation protocol for trajectory prediction is crucial to capture its effectiveness for autonomous driving. Source code along with experimental settings is available online.<br />Comment: NeurIPS 2023 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2306.15136" linkWindow="_blank">http://arxiv.org/abs/2306.15136</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2306.15136
PLink	https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2306.15136
RecordInfo	BibRecord: BibEntity: Subjects: – SubjectFull: Computer Science - Robotics Type: general – SubjectFull: Computer Science - Artificial Intelligence Type: general Titles: – TitleFull: What Truly Matters in Trajectory Prediction for Autonomous Driving? Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tran, Phong – PersonEntity: Name: NameFull: Wu, Haoran – PersonEntity: Name: NameFull: Yu, Cunjun – PersonEntity: Name: NameFull: Cai, Panpan – PersonEntity: Name: NameFull: Zheng, Sifa – PersonEntity: Name: NameFull: Hsu, David IsPartOfRelationships: – BibEntity: Dates: – D: 26 M: 06 Type: published Y: 2023
ResultId	1