Autonomous Driving - MindFlow

Autonomous Driving Data Solution

Help autonomous driving enterprises easily solve infrastructure construction problems

"New engine" for Autonomous Driving

The mainstream algorithm model of autonomous driving is mainly based on supervised in-depth learning. It is an algorithm model based on the derivation of functional relationship between known variables and dependent variables. It needs a large number of structured annotation data to train and optimize the model.

On this basis, if we want to make the autopilot car more intelligent and ensure the application of autonomous driving can form a closed-loop business mode in different vertical landing scenes, then we need massive real road data of high quality to support it.

Whether in the core algorithm training in the fields of environmental perception, accurate positioning, decision-making and planning, control and execution, high-precision map and vehicle networking V2X, or in the construction of data Mid-End infrastructure for autonomous driving AI data, MindFlow autonomous driving data solution can provide complete data service and help autopilot technology release more potential.

Autonomous Driving Data Mid-End Solution

With the digital intelligence platform as the core, build the infrastructure to support large-scale intelligent services, and use the systematic platform products to solve the various needs of users for the generic underlying infrastructure of AI, so that enterprises can continuously turn their business into algorithm models, so as to achieve the purpose of reuse, combinatorial innovation and large-scale construction of intelligent services and business empowerment

Data Annotation Solution

In the field of autonomous driving, the scenes to be labeled in the process of data labeling usually include lane changing and overtaking, passing through the intersection, unprotected left and right turns without traffic light control, and some complex long tail scenes, such as vehicles running red lights, pedestrians crossing the road, vehicles parked illegally on the roadside, etc

Semantic Segmentation

Use polygons to accurately depict all annotation objects in the autopilot scene

3D Doint Cloud - Object Detection

In the 3D point cloud image, the annotation objects such as vehicles and pedestrians are accurately depicted

3D Point Cloud Continuous Frame

In the 3D point cloud continuous frame image, the annotation objects such as vehicles and pedestrians are accurately depicted

Lane Line

Use polylines to accurately depict dimension objects such as lane lines

3D Cube

Use the 3D cube to accurately select the marking objects such as vehicles

2D Boxing for Pedestrians and Vehicles

Use a rectangular box to accurately select vehicles, pedestrians, etc.

Application Case

L3 level autonomous driving system is mainly composed of multi-modules such as perception, location, prediction, decision and control. MindFLow provides customized, scenario-based, and refined data solutions for training and tuning of algorithm models in different modules.

Autonomous Driving Solution of A Cloud Service Manufacturer

Project Background

A training platform is an artificial intelligence development platform independently developed by the manufacturer. It faces the development scenario of deep learning and provides the whole process services from data processing model development, model training to reasoning service.

In order to expand the service capability of the training platform and better serve the autonomous driving application scenario, the manufacturer seeks a stable and reliable autopilot data service solution provider.

Program Content

Combining the characteristics of both sides, MindFlow has customized and developed a set of end-to-end integrated solutions suitable for autonomous driving application scenarios. By joining hands with the manufacturer’s training platform,MindFlow builds a set of end-to-end solutions for the integration of software and hardware from the underlying hardware to data collecting, management, labeling and training, and then to the model application layer, so as to fundamentally solve the problem of building the underlying infrastructure of autonomous driving enterprises. By means of privatization deployment, the customized data service platform is built into the manufacturer's training platform, which can easily handle different data annotation types in autonomous driving scenarios.

3D Doint Cloud - Object Detection

3D Object Detection

Semantic Segmentation

Finally, we can effectively fill the gap in the demand side's autonomous driving data processing capacity. According to the actual project calculation, the average data processing efficiency can be improved by more than 4 to 8 times, and the average data quality can exceed 99%

Why Choose Us

'Understand the data, understand your needs better'

Better Service System

The integrated end-to-end solution can easily solve the problem of infrastructure construction of autonomous driving enterprises