Doordash, Inc.

Jeff Ning Han

Thomas Scott Stephens

Cody Duane Aughney

Dan Michael Babcock

Rohan Balraj Chopra

Harrison Shih

Richard Hwang

Bing Wang

William Preston Parry

Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, an automated vehicle (AV) comprises a body including a storage compartment for storing perishable goods. The storage compartment is accessible by a user upon authentication of the user. The AV further comprises a sensor module for receiving data for navigating the AV. The sensor module is positioned above the body on a support structure at a predetermined height above the ground, such as three to five feet. The data includes one or more of the following: audio data, video data, radio waves, and backscattered light waves. The AV further comprises an onboard computer system configured to process the data to navigate the AV along motor vehicle routes and pedestrian routes. The AV may be configured to interface with an automated locker system to retrieve or deposit the perishable goods.

Automated vehicle for autonomous last-mile deliveries

Provided are systems and processes for optimizing assignments of deliveries for perishable goods. In one aspect, a method is provided for pairing a set of created orders with a set of available couriers. The set of created orders may include orders confirmed by the merchant and the set of available couriers include couriers that are online with an active status. Feasible pairings are generated between each courier and each created order. Infeasible pairings are eliminated based on factors such as transportation mode. Possible routes for each pairing are generated and scored based on weighted factors. The scores are optimized to achieve a set of routes with a maximum score. The routes are then offered to the corresponding courier if the courier will arrive at or after the created order is completed by the merchant. A neural network may be implemented to recognize the optimal score for a given duration.

System and method for dynamic pairing function optimization

Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol which may implement automated order transport and retrieval systems. The method comprises dispatching merchant couriers to transport on-demand orders from merchants to a merchant depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then transported to a customer depot corresponding to an area of delivery destinations. Orders are then assigned to delivery couriers for completion of delivery to customers. Such delivery routing systems and processes may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods. The described mechanisms improve courier efficiency, improve delivery tracking, and reduce overall delivery times.

Depot dispatch protocol for autonomous last-mile deliveries

Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

System for dynamic estimated time of arrival predictive updates

Provided are various mechanisms and processes for optimizing real-time, on-demand delivery service for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Depot dispatch protocol for aggregating on-demand deliveries

Provided are various mechanisms and processes for optimizing real-time, on-demand deliveries of perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

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Palo Alto, CA, United States of America

Jeff Ning Han