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

arXiv: https://arxiv.org/abs/2405.01394 · HTML: https://mlsanity.com/paper/2405.01394/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 5 of 5)

- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Hidden Biases of End-to-End Driving Models](https://mlsanity.com/paper/2306.07957/) (2023)

## Built on by (most cited first, 8 of 8)

- [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025)
- [Hidden Biases of End-to-End Driving Datasets](https://mlsanity.com/paper/2412.09602/) (2024)
- [Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models](https://mlsanity.com/paper/2409.13661/) (2024)
- [Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning](https://mlsanity.com/paper/2406.01544/) (2024)
- [TB-Bench: Training and Testing Multi-Modal AI for Understanding Spatio-Temporal Traffic Behaviors from Dashcam Images/Videos](https://mlsanity.com/paper/2501.05733/) (2025)
- [PRIBOOT: A New Data-Driven Expert for Improved Driving Simulations](https://mlsanity.com/paper/2406.08421/) (2024)
- [OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles](https://mlsanity.com/paper/2410.18112/) (2024)
- [CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving](https://mlsanity.com/paper/2503.01650/) (2025)

## Abstract (arXiv)

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.