AI-Powered Smart Assistant for Meituan Delivery Riders

With the rapid growth of the on‑demand delivery market, improving dispatch efficiency and rider experience has become critical. Meituan’s Rider Smart Assistant was designed to enhance riders’ capabilities through AI, big‑data mining, machine learning, and natural‑language processing.

The system tackles three core challenges: (1) NP‑hard routing and order‑assignment problems that scale exponentially, (2) real‑time decision making within tens of milliseconds, and (3) highly variable delivery scenarios involving weather, traffic, rider skill, and merchant speed.

Meituan’s solution is organized into three layers: the logistics infrastructure layer (city‑wide station layout, manpower, merchant supply), the dynamic supply‑demand balancing layer (pricing mechanisms and real‑time market adjustments), and the real‑time order‑rider matching layer (dispatching and multi‑order route planning).

The voice‑based smart assistant eliminates the need for riders to look at their phones. By using a wake‑free interaction model, riders receive concise voice prompts and respond with simple commands (e.g., “yes” or “no”), reducing the typical five‑to‑six step workflow to one or two steps.

Key AI components include:

Robust speech recognition and voice activity detection to handle noisy environments.

Natural language understanding (NLU) to infer rider intent and extract relevant entities.

Scenario recognition that predicts the next rider context (e.g., arrival at a merchant or user building) and determines the optimal timing for phone calls.

High‑precision ETA prediction covering travel time, vertical movement time, and merchant preparation time.

Massive trajectory data (billions of GPS points per day) is leveraged for navigation refinement, indoor positioning, and activity classification (riding, walking, elevator use). This data improves address parsing, building‑level location accuracy, and dynamic pricing.

Machine‑learning models trained on these features enable real‑time dispatch decisions, dynamic pricing, and personalized rider assistance. The system’s impact includes a significant reduction in manual phone‑call operations, shorter order‑acceptance latency, and decreased rider waiting time at customer locations.

Overall, the smart voice assistant demonstrates how AI, big data, and real‑time optimization can transform same‑city logistics, delivering safer, faster, and more efficient service for both riders and customers.