# CLAD: A realistic Continual Learning benchmark for Autonomous Driving

arXiv: https://arxiv.org/abs/2210.03482 · HTML: https://mlsanity.com/paper/2210.03482/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 74 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 30 of 53)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion](https://mlsanity.com/paper/2111.11326/) (2021)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Rainbow Memory: Continual Learning with a Memory of Diverse Samples](https://mlsanity.com/paper/2103.17230/) (2021)
- [Co2L: Contrastive Continual Learning](https://mlsanity.com/paper/2106.14413/) (2021)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)

## Built on by (most cited first, 30 of 36)

- [TiC-CLIP: Continual Training of CLIP Models](https://mlsanity.com/paper/2310.16226/) (2023)
- [Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning](https://mlsanity.com/paper/2303.14771/) (2023)
- [Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future](https://mlsanity.com/paper/2512.16760/) (2025)
- [Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning](https://mlsanity.com/paper/2407.10281/) (2024)
- [A Practitioner's Guide to Continual Multimodal Pretraining](https://mlsanity.com/paper/2408.14471/) (2024)
- [Optimal Task Order for Continual Learning of Multiple Tasks](https://mlsanity.com/paper/2502.03350/) (2025)
- [Parameter-Efficient Continual Fine-Tuning: A Survey](https://mlsanity.com/paper/2504.13822/) (2025)
- [Continual Learning in the Presence of Repetition](https://mlsanity.com/paper/2405.04101/) (2024)
- [AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation](https://mlsanity.com/paper/2309.10109/) (2023)
- [DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype](https://mlsanity.com/paper/2503.18042/) (2025)
- [PLAN: Proactive Low-Rank Allocation for Continual Learning](https://mlsanity.com/paper/2510.21188/) (2025)
- [FIRE: Frobenius-Isometry Reinitialization for Balancing the Stability-Plasticity Tradeoff](https://mlsanity.com/paper/2602.08040/) (2026)
- [Replay Consolidation with Label Propagation for Continual Object Detection](https://mlsanity.com/paper/2409.05650/) (2024)
- [Continual Hyperbolic Learning of Instances and Classes](https://mlsanity.com/paper/2506.10710/) (2025)
- [RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection](https://mlsanity.com/paper/2508.13878/) (2025)
- [ScrollNet: Dynamic Weight Importance for Continual Learning](https://mlsanity.com/paper/2308.16567/) (2023)
- [Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task](https://mlsanity.com/paper/2405.17779/) (2024)
- [3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding](https://mlsanity.com/paper/2212.06833/) (2022)
- [Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization](https://mlsanity.com/paper/2312.16731/) (2023)
- [CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation](https://mlsanity.com/paper/2401.08940/) (2024)
- [Global Convergence of Continual Learning on Non-IID Data](https://mlsanity.com/paper/2503.18511/) (2025)
- [Lifelong Learning of Video Diffusion Models From a Single Video Stream](https://mlsanity.com/paper/2406.04814/) (2024)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [A Practical Guide to Streaming Continual Learning](https://mlsanity.com/paper/2603.01677/) (2026)
- [TADIL: Task-Agnostic Domain-Incremental Learning through Task-ID Inference using Transformer Nearest-Centroid Embeddings](https://mlsanity.com/paper/2306.11955/) (2023)
- [EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video](https://mlsanity.com/paper/2307.05784/) (2023)
- [CRAM: Large-scale Video Continual Learning with Bootstrapped Compression](https://mlsanity.com/paper/2508.05001/) (2025)
- [Chat with UAV -- Human-UAV Interaction Based on Large Language Models](https://mlsanity.com/paper/2512.08145/) (2025)
- [Continual Cross-Dataset Adaptation in Road Surface Classification](https://mlsanity.com/paper/2309.02210/) (2023)
- [Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments](https://mlsanity.com/paper/2407.14247/) (2024)

## Abstract (arXiv)

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection. The benchmark utilises SODA10M, a recently released large-scale dataset that concerns autonomous driving related problems. First, we review and discuss existing continual learning benchmarks, how they are related, and show that most are extreme cases of continual learning. To this end, we survey the benchmarks used in continual learning papers at three highly ranked computer vision conferences. Next, we introduce CLAD-C, an online classification benchmark realised through a chronological data stream that poses both class and domain incremental challenges; and CLAD-D, a domain incremental continual object detection benchmark. We examine the inherent difficulties and challenges posed by the benchmark, through a survey of the techniques and methods used by the top-3 participants in a CLAD-challenge workshop at ICCV 2021. We conclude with possible pathways to improve the current continual learning state of the art, and which directions we deem promising for future research.