Excellent solutions for automotive electronic data acquisition, calibration and recharging

What is data acquisition, calibration and recharging?

Automotive data collection, calibration and data re-injection are important steps in the vehicle development and testing process. Here is a brief introduction to them:

1. Automobile data collection:

Vehicle data collection refers to the collection of data such as the vehicle's surrounding environment, vehicle status, and driving behavior through sensors, cameras, radars, and other devices installed on the vehicle. These data can include images, videos, lidar scans, inertial measurement unit (IMU) data, GPS location information, etc. The purpose of vehicle data collection is to provide real-time, accurate vehicle and environmental data as input to the autonomous driving system for perception, decision-making, and control.

2. Car calibration:

Automobile calibration is to adjust and optimize the parameters and configuration of the vehicle so that it has good performance and safety in different environments and conditions. Calibration mainly includes calibration requirements, calibration plan formulation, calibration development and verification, extreme environment development and verification, calibration review, calibration release and other steps. In automobile calibration, the collected vehicle data is used to evaluate and adjust the perception, decision-making and control algorithms of the whole vehicle system to adapt to different roads, weather, traffic conditions and other changes. Calibration parameters can include sensor calibration, vehicle dynamics parameters, obstacle detection and tracking algorithms, etc.

Such as: engine management system calibration (including adjustment of parameters such as ignition timing, fuel injection amount, valve timing, etc., to achieve optimal power output, fuel economy and emission control), transmission system calibration (including adjustment of parameters such as automatic transmission shift logic, clutch operating point, transmission ratio selection, etc., to provide smooth shifting and driving comfort), brake system calibration (including adjustment of materials and geometric parameters of brake discs and brake pads, to achieve optimal braking performance and durability), suspension system calibration (including adjustment of parameters such as suspension hardness and damping characteristics, to provide good suspension comfort and handling stability), etc.

3. Data re-injection:

Data reinjection refers to the process of injecting collected actual vehicle data back into the vehicle controller or domain controller for simulation and testing. For example, through data reinjection, actual driving scenarios and situations can be reproduced, and the performance and algorithms of the autonomous driving system can be evaluated and verified. The reinjected data can include sensor data, vehicle status information, and driving behavior data. The purpose of data reinjection is to conduct large-scale testing, verification, and optimization in a simulation environment to accelerate the development and deployment of autonomous driving systems.

These steps play a key role in vehicle development and testing. Through data collection, calibration and feedback, the overall performance, safety and adaptability of the vehicle can be improved and its commercialization process can be accelerated.

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Market background and demand

As of 2021, the automotive data collection market is already quite large and is expected to continue to grow in the next few years. According to market research companies, the global automotive data collection market size in 2021 is about US$15 billion, including related products and services such as on-board sensors, smart vehicle platforms, and data analysis solutions. This is mainly due to the promotion of digital transformation in the automotive industry and the increasing emphasis on vehicle data.

The growth of the automotive data collection market is driven by multiple factors. First, automakers and technology companies are increasingly paying attention to the collection and analysis of vehicle data to provide better driving experience, vehicle safety, and vehicle maintenance. Second, the rise of new energy vehicles and autonomous driving technology has created a greater demand for data collection and analysis. In addition, the enhanced connectivity of vehicles with other smart devices and systems has also contributed to the growth of the automotive data collection market.

The development of new energy vehicles and autonomous driving technology has had a significant impact on vehicle data collection, mainly in the following aspects:

1. Increase in data volume:

The introduction of new energy vehicles and autonomous vehicles has led to a significant increase in the amount of data generated by vehicles. Electric vehicles contain a large amount of data related to batteries, motors, energy management, etc., while autonomous vehicles collect information about the surrounding environment through various sensors such as lidar, cameras, and radar. The large-scale data generated by these vehicles needs to be collected, transmitted, stored, and analyzed to support vehicle control, performance optimization, and safety requirements.

2. Diversified data types:

New energy vehicles and autonomous driving technologies have introduced a variety of new data types. For example, electric vehicles need to collect battery status and performance data for battery management and charging strategy optimization. Autonomous vehicles need to collect environmental perception data, vehicle status data, and behavior data to support autonomous decision-making and driving control. Therefore, automotive data acquisition systems need to be able to process and analyze a variety of data types to provide more comprehensive information support.

3. Data security and privacy protection:

As the amount of automotive data increases, data security and privacy protection become more important issues. New energy vehicles and autonomous vehicles carry a lot of sensitive information, such as vehicle location, driving behavior, and vehicle performance. Therefore, the automotive data collection system needs to take corresponding security measures to protect the confidentiality and integrity of the data and prevent the data from being illegally accessed and abused.

4. Increased real-time requirements:

Autonomous vehicles have an even more urgent need for real-time data. For example, vehicles need to collect and process sensor data in real time in order to accurately perceive and make decisions about the surrounding environment. Therefore, the automotive data acquisition system needs to have fast and efficient data acquisition and processing capabilities to meet real-time requirements.

5. Safety and driving assistance systems:

Autonomous vehicle calibration needs to focus on parameter adjustment of safety and driver assistance systems. This includes calibration of functions such as automatic braking systems, adaptive cruise control, lane keeping assist, etc. to ensure their safety and reliability in different road and traffic conditions.

In general, the development of new energy vehicles and autonomous driving technology has promoted the advancement and application of automotive data collection technology. They have put forward higher requirements on data volume, data type, data security and real-time performance, and promoted the innovation and development of data collection technology. At the same time, it has also provided a richer foundation for the fields of intelligent transportation, vehicle management and driving assistance.

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Market status and industry pain points

With the development of new energy vehicles and autonomous driving technology, the current automotive data collection industry faces some new pain points and challenges, including the following:

1. Data standardization and interoperability: Due to different data formats and interface standards adopted by different automakers and technology suppliers, data collection interoperability issues arise. This complicates data collection and integration, limiting the effective use and sharing of data. The industry needs more extensive data standardization efforts to promote data interoperability and interoperability.

2. Data security and privacy protection: With the increase in vehicle data and the rise in sensitivity, data security and privacy protection have become the focus of industry attention. The automotive data collection system needs to take strict security measures to protect data from malicious attacks and illegal access. At the same time, it is necessary to ensure that the personal privacy of car owners and drivers is properly protected in accordance with relevant laws and regulations and policy requirements.

3. Data quality and accuracy: The process of collecting automotive data may face challenges in data quality and accuracy. Sensor errors, noise and failures may cause inaccurate or incomplete data. The data collection system needs to have high accuracy and reliability to ensure that the collected data quality meets the requirements.

4. Challenges in processing and storage: As the amount of data increases, challenges in data processing and storage also arise. Large-scale data processing requires high-performance computing and storage resources to support real-time analysis and decision-making. In addition, long-term storage and management of large amounts of data also require solutions to data storage and backup issues.

5. Regulations and compliance requirements: Automotive data collection involves a series of regulations and compliance requirements, including data privacy, data security, data use and sharing. The industry needs to comply with relevant regulations and policies and ensure that the data collection and processing process complies with legal and ethical requirements.