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Jun 17, 2021 · Artificial Intelligence

MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi‑Task Learning

The paper introduces MTrajRec, a Seq2Seq multi‑task learning framework that simultaneously restores low‑sampling‑rate GPS trajectories to high‑sampling‑rate and aligns them to the road network, achieving more accurate and efficient trajectory recovery for downstream applications such as navigation and travel‑time estimation.

Deep LearningKDD 2021Seq2Seq

0 likes · 9 min read

MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi‑Task Learning

Didi Tech

Sep 29, 2020 · Artificial Intelligence

Technical Overview of Didi’s MJO 3D Panoramic Navigation, Main/Sub‑Road Yaw Detection, and Deep‑Learning‑Based Navigation Engine

Didi’s Navigation system combines a novel MJO 3D panoramic map with advanced data‑compression and octree rendering, precise main/sub‑road yaw detection using LSTM‑based models trained on GPS and image data, and a lightweight deep‑learning engine optimized for mobile CPUs/GPUs, delivering accurate, real‑time guidance for ride‑hailing and autonomous driving.

3D renderingDeep LearningGPS trajectory

0 likes · 21 min read

Technical Overview of Didi’s MJO 3D Panoramic Navigation, Main/Sub‑Road Yaw Detection, and Deep‑Learning‑Based Navigation Engine

Amap Tech

Mar 6, 2020 · Industry Insights

How Mobile and Car Navigation Achieve Precise Positioning: Sensor Fusion, Map Matching, and High‑Precision Evolution

This article systematically explains the key technologies behind mobile and vehicle navigation positioning, covering sensor fusion, AHRS, map‑matching algorithms based on hidden Markov models, Kalman filtering, and the evolution toward lane‑level and centimeter‑level accuracy for autonomous driving.

HMMKalman filterSensor Fusion

0 likes · 14 min read

How Mobile and Car Navigation Achieve Precise Positioning: Sensor Fusion, Map Matching, and High‑Precision Evolution

Zhengtong Technical Team

Dec 20, 2019 · Big Data

Optimizing Trajectory Visualization: From Data Collection to Rendering

This article examines the challenges of mobile‑based trajectory tracking in city management and presents a comprehensive set of optimizations—including adaptive GPS sampling, keep‑alive strategies, accuracy enhancements, algorithmic fitting, and cinematic animation effects—to produce smooth, accurate, and visually appealing trajectory displays at scale.

GPSKalman filterTrajectory

0 likes · 11 min read

Optimizing Trajectory Visualization: From Data Collection to Rendering

Amap Tech

Aug 13, 2019 · Artificial Intelligence

Multi‑Sensor Fusion Positioning for Vehicle Navigation: GPS/IMU/Map‑Matching Solution

Gaode's solution combines GPS, IMU, odometer, visual sensors with map‑matching using a Kalman filter, addressing yaw drift, loss of fix, and road‑capture errors in vehicle navigation, especially in urban canyons, achieving over 90% road identification and significant error reductions while keeping hardware costs low.

GNSSIMUKalman filter

0 likes · 16 min read

Multi‑Sensor Fusion Positioning for Vehicle Navigation: GPS/IMU/Map‑Matching Solution

Amap Tech

Jun 28, 2019 · Fundamentals

Road Matching: Definitions, Applications, and Key Algorithms

Road matching, a core subset of map‑matching theory, aligns GPS points to the correct road segments using algorithms such as distance‑based measures, Fréchet‑distance global optimization, and Hidden Markov Models, enabling accurate navigation, heterogeneous data fusion, traffic analysis, and urban planning, as validated by ACM SIGSPATIAL competitions.

Fréchet distanceGISHMM

0 likes · 11 min read

Road Matching: Definitions, Applications, and Key Algorithms

21CTO

Feb 19, 2016 · Artificial Intelligence

How to Achieve Accurate GPS Map Matching with ST‑Matching: A Practical Guide

This article reviews map‑matching challenges caused by GPS errors, categorizes existing algorithms, describes the ST‑Matching approach used on Washington State road data, and outlines key implementation techniques such as projection handling, memory‑pool loading, A* shortest‑path search, and localized indexing to improve accuracy and performance.

GPSST-Matchingmap matching

0 likes · 8 min read

How to Achieve Accurate GPS Map Matching with ST‑Matching: A Practical Guide

21CTO

Aug 8, 2015 · Artificial Intelligence

How to Achieve High-Accuracy GPS Map Matching: Algorithms, Data, and Implementation

This article reviews map‑matching challenges, surveys algorithm categories, describes the ST‑Matching method and its implementation details, and presents experimental results using a large road network and GPS data to achieve high‑accuracy trajectory alignment.

GPSST-MatchingSpatial Data

0 likes · 8 min read

How to Achieve High-Accuracy GPS Map Matching: Algorithms, Data, and Implementation