Smart Return System: Optimizing Bike Return Experience Using Sensor Data and Trajectory Prediction

The proposed smart return system for shared‑mobility bikes uses accelerometer data and Hidden Markov Model trajectory prediction to distinguish temporary stops from actual parking, enabling 85% of returns to be pre‑judged, improving accuracy, speed, user experience, and operational costs.

Hidden Markov ModelsLocation AccuracySmart Transportation

0 likes · 5 min read

Smart Return System: Optimizing Bike Return Experience Using Sensor Data and Trajectory Prediction

Meituan Technology Team

Jan 28, 2021 · Artificial Intelligence

Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge

Meituan’s unmanned delivery team secured first place in the Generalizability track and second in the Regular track of the NeurIPS 2020 INTERPRET trajectory‑prediction challenge by employing a mixed‑attention graph‑transformer with dual‑channel GRU and adaptive map processing, achieving ADEs of 0.5339 m and 0.1912 m respectively.

Graph Neural NetworkNeurIPSautonomous vehicles

0 likes · 15 min read

Trajectory Prediction Algorithm for Autonomous Vehicles: Winning Solutions in NeurIPS 2020 INTERPRET Challenge

Meituan Technology Team

Oct 31, 2019 · Artificial Intelligence

Meituan Unmanned Delivery Team Wins CVPR 2019 Obstacle Trajectory Prediction Challenge – Methodology Overview

Meituan’s unmanned‑delivery team won the CVPR 2019 Trajectory Prediction Challenge by using a multi‑class independent LSTM encoder‑decoder with Gaussian‑noise augmentation, discarding size and noisy orientation data, applying rotation and interpolation augmentations, and achieving a weighted ADE of 1.3425, surpassing StarNet and TrafficPredict, with plans to explore interaction‑based and graph‑neural‑network models.

CVPR 2019LSTMMeituan

0 likes · 9 min read

Meituan Unmanned Delivery Team Wins CVPR 2019 Obstacle Trajectory Prediction Challenge – Methodology Overview