Apollo V1.2 Botnet Information Gathering tool

We’re pleased to present. Apollo v1.27. Apollo is our cloud service responsible for all data moving in or out of our databases, 

Apollo v1.2 Client 3.0 from previous versions of Apollo Client. To illustrate the migration process, …




Updating your app to Angular Apollo 2.0 and Apollo Client 3.0. … The previous version of Apollo Angular (v1.0) setup had two extra packages: …

Apollo is our cloud service responsible for all data moving in or out of our databases, as well as handling all communications to Pods in the field (receiving sensor data, sending firmware updates, handling payment enabling/disabling logic). Apollo is named after the god of knowledge, as it is the centre point of all our data!

Previously, load usage calculations (and therefore billing) were based on load sensor readings being sent from each Pod to the cloud. These readings are stored onboard the Pods local memory storage in the form of snapshots, but if a loss in the connection between the Pod and cloud persists, the accumulation of sensor reading snapshots can easily exceed the limit of local storage which results in gaps in the data. Without this data, we don’t know how much energy a household consumed, and our payments system cannot bill accordingly.




Several firmware releases ago, the Pod began to keep a rolling track of total consumption data points and storing it every hour. It can store up to 1 week’s worth of hourly values but even if the pod is offline longer than that, the total usage is still captured (just not with hourly granularity).

This Apollo cloud release processes and integrates this rolling usage value into existing billing architecture, making billing resilient to patchy data and also making it possible to bill for days past that could not be billed for the reasons mentioned above. All in all, this feature eliminates lost consumption data and enables our customers to recover lost revenue.

Apollo v1.2 Botnet incorporates new deep learning models to enhance the capabilities for certain Apollo modules. This version works seamlessly with new additions of data pipeline services to better serve Apollo developers. Apollo 6.0 is also the first version to integrate certain features as a demonstration of our continuous exploration and experimentation efforts towards driverless technology.

Major Features and Improvements

  • Upgraded Deep Learning Models
    • PointPillars based obstacle detection model
    • Semantic map based pedestrian prediction model
    • Learning based trajectory planning model
  • Brand New Data Pipeline Services
    • Low speed obstacle prediction model training service with semantic map support
    • PointPillars based obstacle detection model training service
    • Control profiling service
    • Vehicle dynamic model training service
    • Open space planner profiling service
    • Complete control parameter auto-tune service
  • Driverless Research
    • Remote control interface with DreamView integration
    • Audio based emergency vehicle detection system
  • Upgraded dev environment including build and dependency updates

[Note] All models and methodologies included in Apollo v1.2 botnet are for research purposes only. Productized and commercial uses of these models are NOT encouraged, and it is at your own risk. Please be cautious to try Apollo 6.0 with enough safety protection mechanism. Your feedback is highly appreciated for us to continuously improve the models.




Apollo v1.2 Botnet enhances the complex urban road autonomous driving capabilities of previous Apollo releases, by introducing curb-to-curb driving support. With this new addition, Apollo is now a leap closer to fully autonomous urban road driving.
The car has complete 360-degree visibility, along with upgraded perception deep learning model a brand new prediction model to handle the changing conditions of complex road and junction scenarios, making the car more secure and aware. New Planning scenarios have been introduced to support curb-side functionality.

Major Features And Improvements

  • Brand new Data Pipeline Service
    • Sensor Calibration service
  • Brand new module – Storytelling
  • Scenario – Based Planning with a new planning scenarios to support curb-to-curb driving
    • Park-and-go
    • Emergency
  • Prediction Model – Caution Obstacle
    • Semantic LSTM evaluator
    • Extrapolation predictor
  • Control module
    • Model Reference Adaptive Control (MRAC)
    • Control profiling service
  • Simulation scenarios

Autonomous Drive Capabilities

Vehicles with this version can drive autonomously in complex urban road conditions including both residential and downtown areas. BE CAUTIOUS WHEN DRIVING AUTONOMOUSLY, ESPECIALLY AT NIGHT OR IN POOR VISION ENVIRONMENT. URBAN DRIVING INVOLVES NAVIGATING HIGH RISK ZONES LIKE SCHOOLS, PLEASE TEST APOLLO v1.2 Botnet WITH THE SUPPORT FROM APOLLO ENGINEERING TEAM, PLEASE AVOID DRIVING THE VEHICLE ON THE HIGHWAY OR AT SPEEDS THAT ARE ABOVE OUR SUPPORTED THRESHOLD.

Apollo 5.0 is an effort to support volume production for Geo-Fenced Autonomous Driving. The car now has 360-degree visibility, along with upgraded perception deep learning model to handle the changing conditions of complex road scenarios, making the car more secure and aware. Scenario-based planning has been enhanced to support additional scenarios like pull over and crossing bare intersections.

Major Features And Improvements

  • Brand new Data Pipeline Service
    • Vehicle Calibration
  • New Perception algorithms
  • Sensor Calibration Service
  • Scenario – Based Planning with a new planning algorithm, Open Space Planner and new scenarios supported
    • Intersection – STOP Sign, Traffic Light, Bare Intersection
    • Park – Valet, Pull Over
  • Map Data Verification tool
  • Prediction Evaluators
  • Simulation web platform – Dreamland
    • Scenario Editor
    • Control-in-loop Simulation
  • Cyber RT runtime framework
    • Official ARM CPU support with full docker dev environment support: https://github.com/ApolloAuto/apollo/blob/master/docs/cyber/CyberRT_Docker.md
    • Python language support for full set of Cyber RT API
    • Cyber RT API website goes online: www.masterscyber.com

Download Apollo V1.2 Botnet

Zip Password : www.masterscyber.com