Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey
cs.LG, cs.AI, cs.CV, cs.RO, cs.SY, eess.SY
Submitted: 2023-04-21
Updated: 2026-08-28
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Research on vehicle detection based on improved YOLOv8 network
- BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
- BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregation
- Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
- DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
- DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
- Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
- LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
- FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection
- BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
- MapKD: Unlocking Prior Knowledge with Cross-Modal Distillation for Efficient Online HD Map Construction
- OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework
- FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning
- DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- First-Order Error Matters: Accurate Compensation for Quantized Large Language Models
- CDQuant: Greedy Coordinate Descent for Accurate LLM Quantization
- Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
- A Simple and Effective Pruning Approach for Large Language Models
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