Pith. sign in

REVIEW 2 cited by

Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.01394 v1 pith:3XYCC5ZV submitted 2024-03-21 cs.AI

Weize Zhang , Mohammed Elmahgiubi , Kasra Rezaee , Behzad Khamidehi , Hamidreza Mirkhani , Fazel Arasteh , Chunlin Li , Muhammad Ahsan Kaleem
show 3 more authors
Eduardo R. Corral-Soto Dhruv Sharma Tongtong Cao
This is my paper · ORCID
classification cs.AI
keywords solutionarchitecturedrivingautonomouscarlachallengeimpactleaderboard
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place. We employed a modular architecture for our solution consists of five main components: sensing, localization, perception, tracking/prediction, and planning/control. Our solution leverages state-of-the-art language-assisted perception models to help our planner perform more reliably in highly challenging traffic scenarios. We use open-source driving datasets in conjunction with Inverse Reinforcement Learning (IRL) to enhance the performance of our motion planner. We provide insight into our design choices and trade-offs made to achieve this solution. We also explore the impact of each component in the overall performance of our solution, with the intent of providing a guideline where allocation of resources can have the greatest impact.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hidden Biases of End-to-End Driving Datasets

    cs.CV 2024-12 conditional novelty 6.0 of 10

    By retraining a standard TransFuser++ policy with the PDM-Lite rule-based expert on CARLA Leaderboard 2.0, the authors show that dataset choices dominate performance, set new state-of-the-art results, and identify an ...

  2. A Survey on Vision-Language-Action Models for Autonomous Driving

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.

Pith tools