End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
cs.RO, cs.CV, cs.LG
Submitted: 2026-10-01
Updated: 2026-10-01
Terminology
Sources
- End to End Learning for Self-Driving Cars
- Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
- Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Mask R-CNN
- You Only Look Once: Unified, Real-Time Object Detection
- SSD: Single Shot MultiBox Detector
- Focal Loss for Dense Object Detection
- EfficientDet: Scalable and Efficient Object Detection
- Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
- Computing Systems for Autonomous Driving: State-of-the-Art and Challenges
- Sampling-based Algorithms for Optimal Motion Planning
- Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
- Playing Atari with Deep Reinforcement Learning
- Prioritized Experience Replay
- Proximal Policy Optimization Algorithms
- DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
- Urban Driving with Conditional Imitation Learning
- Deep Reinforcement Learning for Autonomous Driving: A Survey
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- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving