Autonomous Cars

The module configuration of the self-driving system is like simulating a human driving a vehicle. First, humans must recognize the vehicle’s surrounding environment through their eyes and use their brains to decide the vehicle’s driving path. Finally, use their hands and feet to control the steering and braking of the vehicle.

Car, throttle, let the vehicle drive safely to the destination. The eyes of a self-driving car are sensors such as lidar, radar, and camera. The original data is analyzed through algorithms to identify the type and location of obstacles. Because different types of sensors have different resolution capabilities for obstacles, such as the camera for obstacles. The color or appearance recognition ability is strong, the lidar has a solid ability to detect the position and detection range of obstacles, and the radar has a strong detection distance ability to metal obstacles, so the characteristics of obstacles detected by the sensor are analyzed—perceptual fusion, calculating the obstacle information of the environment around the self-driving car.

After the self-driving car analyzes the environmental information, it still needs to know the location and driving destination of the self-driving car. Therefore, it is necessary to receive GPS, IMU, dynamic vehicle information, sensor environmental feature detection, and high-precision positioning through the cooperative positioning module. The self-driving car’s location and destination driving trajectory are obtained after calculating the map data. Next, the decision-making module uses the environmental information of the multi-sensory fusion module, the location of the vehicle, and the destination track of the collaborative positioning module to carry out dynamic decision-making design. It displays the information status of the self-driving car on the human-machine interface and background management.

High-resolution maps are an integral part of self-driving technology. Compared with ordinary maps, high-precision maps require more detailed information on roads, traffic signs, lane lines, obstacles, etc., and higher geolocation accuracy. This map data is the basis for self-driving sensors and positioning systems that provide more accurate location and environmental awareness. Establishing high-precision maps requires combining satellite surveying and mapping technology, ground surveying vehicles, laser scanning, and image processing technologies to collect and organize road-related information. These map data need to be updated and maintained in real-time to deal with road changes, construction sites, and traffic incidents.

Decision-making control technology is the key to realizing the intelligent driving of self-driving cars. Based on the information provided by sensors and positioning systems, self-driving cars need to be able to perform real-time path planning, obstacle identification, and traffic behavior prediction and make corresponding decisions and control actions. This involves applying artificial intelligence techniques like deep, machine, and reinforcement learning. Self-driving cars must understand and predict dynamic changes in the surrounding environment, such as other vehicles, pedestrians, traffic signals, etc., and make adaptive decisions in different situations. Decision-making control technology also needs to consider the balance of security and performance. Self-driving cars must ensure safe driving, obey traffic laws and norms, and respond appropriately when encountering danger or emergencies. At the same time, self-driving cars also need efficient driving strategies, such as reducing driving time and energy consumption.

As a self-driving vehicle, a self-driving car needs to interact well and communicate with passengers or users. Human-computer interaction technology includes voice commands, gesture recognition, face recognition, touch interface, etc., enabling passengers to conveniently communicate and operate self-driving cars. Human-computer interaction technology also involves designing interior infotainment and passenger safety monitoring systems. Self-driving cars must provide rich entertainment and information services while ensuring the safety and comfort of passengers.

The development of self-driving car technology must pay attention to safety and privacy protection. Self-driving cars need reliable security mechanisms to prevent the system from being hacked or maliciously interfered with. This includes securing the vehicle’s communications and control systems and ensuring that the vehicle’s data and operation are not tampered with or tampered with by unauthorized persons. At the same time, self-driving cars also need to protect the privacy of passengers and pedestrians. Self-driving cars usually collect and process a large amount of sensing data and location information, so strict privacy protection policies and technical measures must be formulated to ensure the security and confidentiality of personal data.

SINTRONES’ high-performance computer system provides several vital details and functions for autonomous cars driving and unmanned vehicle applications, including:

Powerful computing capability: SINTRONES adopts high-performance processors and graphics cards from Intel and NVIDIA. These processors have powerful computing capabilities and can handle complex data calculations and intelligent algorithms to achieve efficient autonomous driving functions. The computer system of SINTRONES has high-performance processors and graphics cards, which can handle massive data and complex computing tasks. It can process and analyze data from multiple sensors in real time and make quick decisions and reactions. This is critical for an autonomous driving system, as it needs to acquire data from various sensors (such as radar, camera, lidar, etc.) in real-time for object detection, route planning, decision-making, and more.

Wireless communication module: The system integrates various wireless communication modules, including 4G/5G, Wi-Fi, and Bluetooth. These communication modules enable high-speed data transmission and communication, supporting the connection between unmanned vehicles and other devices, infrastructure, and cloud systems.

Power noise and interference protection: The system uses a remote power supply unit, serial ports, and input and output ports to protect the system from power noise and interference. These designs can reduce external power supply interference to the system and improve the stability and reliability of the system.

Broad voltage input and comprehensive temperature operation: The system has a wide voltage input range and can adapt to the power input requirements of different vehicles. In addition, the system also has a wide temperature operating range and can operate normally in extreme temperature environments to ensure system reliability and performance.

Fanless design: The system is designed as a fanless structure, eliminating the risk of fan noise and mechanical failure. This is very important for unmanned vehicle applications because the fanless design can reduce noise interference while improving the reliability and life of the system.

Multiple communication protocol support: the system supports multiple communication protocols, such as CAN, RS232, and RS485.

High scalability: The system has good scalability and can be configured and expanded according to application requirements. It supports connecting multiple external devices, such as camera lenses, radar, and optical radar, which can meet the needs of different application scenarios.

Low power consumption and high efficiency: SINTRONES’ computer system design is energy-efficient and efficient and can provide excellent computing performance under low power consumption. This is especially important for unmanned vehicle applications, extending battery life and increasing system runtime.

High reliability and security: SINTRONES’ high-efficiency computer system is designed with reliability and security in mind. It adopts strict hardware and software design standards to ensure system reliability and security. This computer system has protection mechanisms to prevent potential attacks and malfunctions. At the same time, it also has a fault tolerance capability, which can continue to operate or enter a safe mode in the event of a fault to ensure the safe operation of unmanned vehicles.

Real-time perception and decision-making: SINTRONES’ computer system can sense changes in the surrounding environment in real-time, including vehicles, pedestrians, traffic signs, etc., and make accurate decisions based on this information. It has advanced sensing technology and algorithms capable of visual recognition, path planning, and traffic coordination functions.

Software and hardware integration and optimization: SINTRONES regards software and hardware integration and optimization as one of the critical details. Hardware design and software development work together to achieve higher performance and efficiency. This integration and optimization can improve the system’s operating efficiency while simplifying the development and maintenance work.

High temperature and shock resistance: Autonomous driving and unmanned vehicle applications often face extreme environmental conditions, such as high temperature and vibration. The computer system of SINTRONES has high temperature and shock resistance and can operate normally in harsh environments to ensure the stability and reliability of the system.

High-speed data aggregation and communication interface: SINTRONES’ computer system has high-speed data aggregation and communication interface, which can realize efficient communication and data exchange with sensors, controllers, and other systems. This facilitates immediate sensor data collection, processing, and feedback to ensure the accuracy and safety of autonomous driving systems.

Machine learning and artificial intelligence support: SINTRONES’ computer system supports the execution and optimization of machine learning and artificial intelligence algorithms. This enables the system to learn and extract patterns from large amounts of data for intelligent decision-making and prediction, thereby improving the performance and autonomy of autonomous driving systems.