AVstack: An Open-Source, Reconfigurable Platform for Autonomous Vehicle Development
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Abstract (arXiv)
Pioneers of autonomous vehicles (AVs) promised to revolutionize the driving experience and driving safety. However, milestones in AVs have materialized slower than forecast. Two culprits are (1) the lack of verifiability of proposed state-of-the-art AV components, and (2) stagnation of pursuing next-level evaluations, e.g., vehicle-to-infrastructure (V2I) and multi-agent collaboration. In part, progress has been hampered by: the large volume of software in AVs, the multiple disparate conventions, the difficulty of testing across datasets and simulators, and the inflexibility of state-of-the-art AV components. To address these challenges, we present AVstack, an open-source, reconfigurable software platform for AV design, implementation, test, and analysis. AVstack solves the validation problem by enabling first-of-a-kind trade studies on datasets and physics-based simulators. AVstack solves the stagnation problem as a reconfigurable AV platform built on dozens of open-source AV components in a high-level programming language. We demonstrate the power of AVstack through longitudinal testing across multiple benchmark datasets and V2I-collaboration case studies that explore trade-offs of designing multi-sensor, multi-agent algorithms.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Dec 2022.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 1.
6 papers.
- 2025LLaViDA: A Large Language Vision Driving Assistant for Explicit Reasoning and Enhanced Trajectory Planningcited by 5
- 2023V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integrationcited by 2
- 2024EI-Drive: A Platform for Cooperative Perception with Realistic Communication Modelscited by 1
- 2025Assured Autonomy with Neuro-Symbolic Perceptioncited by 1
- 2025EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Drivingcited by 0
- 2026Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systemscited by 0
- 2026Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems3 Jun 2026
- 2025LLaViDA: A Large Language Vision Driving Assistant for Explicit Reasoning and Enhanced Trajectory Planning20 Dec 2025
- 2025Assured Autonomy with Neuro-Symbolic Perception27 May 2025
- 2025EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving26 Feb 2025
- 2024EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models13 Dec 2024
- 2023V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration26 Sep 2023