Official Amap technology account showcasing all of Amap's technical innovations.
FSDrive introduces a spatio‑temporal chain‑of‑thought approach that enables visual language models to generate future driving scenes as images, improving trajectory planning accuracy and safety by eliminating cross‑modal gaps and enforcing physical constraints in autonomous driving.
HS‑STAR introduces a three‑stage hierarchical sampling framework that identifies high‑utility boundary problems, reallocates computation budget to them, and fine‑tunes large language models, achieving significant accuracy gains on math reasoning benchmarks without extra sampling cost.
This article introduces Pos2Distill, a novel knowledge‑distillation framework that transfers capabilities from advantageous to disadvantaged positions in large language models, effectively mitigating positional bias and improving performance on long‑text retrieval and in‑context reasoning tasks.
This article explores how Gaode leverages AI and large‑scale spatio‑temporal pre‑training to transform uncertain urban transit into reliable, real‑time bus and subway predictions, detailing technical challenges, model architectures, and user‑focused features that enhance daily commuting.
Omni-Effects introduces a unified framework that combines LoRA‑based expert mixture models and spatially aware prompts to generate multiple, precisely placed visual effects in video, supported by the new Omni‑VFX dataset and evaluation suite, demonstrating superior spatial control and diversity over prior single‑effect methods.
This article describes how the Gaode terminal team tackled large‑scale repository upgrades by building a code‑RAG and code‑Agent tool, addressing recall and stability issues, then fine‑tuning a small LLM (Qwen3‑4B) with LoRA and custom datasets to achieve reliable, low‑cost, on‑device code‑query performance.
The paper introduces FingER, an entity-level evaluation framework and the FingER-Instruct-60k dataset for assessing AI-generated video quality with fine-grained reasoning, and demonstrates state-of-the-art zero-shot performance on multiple benchmarks using novel training strategies.
This article introduces FingER, a novel entity‑level evaluation framework and the FingER‑Instruct‑60k dataset for assessing AI‑generated video quality with fine‑grained reasoning, and demonstrates its state‑of‑the‑art performance on multiple benchmarks using advanced training strategies such as GRPO.
The UPRE framework introduces multi‑view domain prompts and unified representation enhancement to achieve zero‑shot domain adaptation for object detection, dramatically improving detection accuracy on unseen target domains across diverse visual scenarios.
The UPRE paper, presented at ICCV, introduces a multi‑view domain prompt and a unified representation enhancement to enable zero‑shot domain adaptation for object detection, achieving state‑of‑the‑art performance across diverse weather, geographic, and synthetic‑to‑real scenarios.
