#autonomous-driving
7 articles
01
·★ MEMBER·PAPER·12 min read
Paper walkthrough: Qwen-Drive-1.0 — bolting 3D perception and planning onto a VLM without touching its architecture
Leave the pretrained VLM exactly as it is, attach a BEV perception head and a Planning Expert, and you get 3D detection, occupancy, maps and trajectory generation in one model — general vision-language ability nearly intact, 90.7 PDMS on NAVSIM. A ground-up walkthrough, straight from the paper.
02
·Paper Deep-Dives·FREE·13 min read
When Proxies Stop Being Good Enough — Reading August 2026's Eight Autonomous Driving Papers Together
A cross-cutting read of eight autonomous-driving arXiv papers from late August 2026. Three groups independently stop measuring safety in expectation, two add an observation channel outside the ego vehicle's own history, and two genuinely don't fit the story.
03
·Agents·★ MEMBER·PAPER·9 min read
End-to-End Driving from Scratch — Perception to Control in a Single Network
End-to-end autonomous driving learns everything from camera pixels to steering in one neural network. Starting from zero, we cover how it differs from the modular stack, imitation learning and distribution shift, why 'predicting the average' causes crashes, and why interpretability and validation remain the hard part.
04
·★ MEMBER·PAPER·9 min read
Paper explained: DEFT-RLVR — show a driving VLM the future trajectory too early and it fabricates its reasoning
Let a teacher model peek at the ground-truth trajectory while writing chain-of-thought annotations for driving, and severe hallucinations double from 29% to 50% — the paper calls this trajectory anchoring bias. This article walks through AD-MCQ, which turns planning into a verifiable multiple-choice task, and DEFT-RLVR (arXiv:2608.01755), which reveals trajectories only after the model commits to a decision, using nothing but the paper itself as the source.
05
·CNNs & Image Recognition·FREE·9 min read
The Autonomous Driving Perception Stack from Scratch — What Cameras and LiDAR Each Bring to the Table
How a self-driving car figures out what is around it, starting from sensor physics. We cover the complementary strengths of cameras and LiDAR, the bird's-eye-view grid where all sensors meet, occupancy grids grown by Bayesian updates, and the tug-of-war between safety requirements and compute budgets — no prior knowledge assumed.
06
·VLMs & Multimodal·★ MEMBER·PAPER·8 min read
BEV Representations From Scratch — Fusing Multiple Cameras Into One Top-Down Map
How a self-driving car turns six camera feeds into a single top-down map. Starting from perspective projection, we build up to the two big design philosophies: LSS, which pushes features into 3D via a predicted depth distribution, and Transformer-style methods like BEVFormer that pull information with BEV queries.
07
·Paper Deep-Dives·★ MEMBER·PAPER·8 min read
Paper Walkthrough: Alpamayo — NVIDIA's Reasoning Model for Autonomous Driving
A first-principles read of NVIDIA's driving VLA Alpamayo-R1: the Chain of Causation dataset that structures reasoning as cause and effect, flow-matching trajectory decoding that fits in 99 ms, and the three-stage training recipe that uses RL to punish gaps between what the model says and what it drives.