AI/Big Model - Robot 

 1. Application of large language model (LLM) in the field of robotics 

 The multimodal large language model (LLM) is gradually being applied to robot control and operation tasks. For example, ManipLLM is a large language model that combines multimodal input, which can perform complex object manipulation tasks. This model promotes the robot's autonomous decision-making ability in a dynamic environment by learning the combination of visual, language and physical interaction. 

 The essence of this technology is that through the combination of LLM and robot perception ability, the robot's understanding and response speed of complex operation scenarios are improved, and the gap between robots and human operators in object operation is further narrowed. 

 2. Extended applications of mobile robots (AMRs) 

 By 2024, mobile robots (AMRs) had shifted from early experimental applications to large-scale deployment, especially in the fields of warehousing and logistics, manufacturing and agriculture. The global mobile robot market is expected to reach $40.6 billion in 2028, showing the huge growth potential of this technology. The core technological breakthrough of this robot lies in the improvement of its perception and data processing ability, which can better meet the challenges of unstructured environments. This means that robots are not limited to industrial production, but can also perform tasks in outdoor environments, such as crop transportation and lawn mowing on farms. 

 3. The development of collaborative robots (Cobots) 

 Cobots have become an important part of industry and services, especially in 2024, which are becoming more intelligent and safer with the progress of sensors and visual technology. Collaborative robots can perceive environmental changes in real time and cooperate more safely and flexibly with human operators. In the next few years, the application field of collaborative robots will gradually expand from industrial manufacturing to medical, service and home assistance scenarios. 

 4. Breakthrough of AR and VR technology in the field of robotics 

 Augmented reality (AR) and virtual reality (VR) technologies provide new training methods for robot operators. This kind of technology helps operators learn complex robotic operations more safely and efficiently by building a virtual training environment. For example, AR can superimpose robotic fault diagnosis data in real time to help technicians solve problems quickly, while VR provides fully simulated training scenarios, greatly reducing risks and costs in real-world operations. This technology has also been widely used in robot remote control in dangerous environments. 

 5. Application of digital twinning technology 

 Digital twinning technology has become a key tool for optimizing the performance of robots. This technology improves the operation efficiency and reliability of the robot by creating virtual copies of the physical system and monitoring and analyzing the state and performance of the robot in real time. In 2024, with the widespread application of digital twin technology, robots were able to conduct a large number of virtual tests before actual deployment, reducing system failure and operation risk. 

 6. Innovation in robot programming languages: the rise of Rust 

 In 2024, the Rust language was widely used in the field of robot programming. Due to its memory security and efficient management, Rust is suitable for use in complex robot systems. This enables robot developers to build more stable and secure systems, especially in scenarios with high real-time requirements. Rust has gradually replaced the traditional C language as one of the mainstream choices. 

 AI/Big Model - Self-driving 

 1. Application of large model in self-driving 

 Large model (LLM) has gradually become one of the core technologies in the field of autonomous driving. Many companies around the world, such as Tesla, Waymo and Nvidia, are vigorously exploring the application of large models in perception, decision-making and control modules. Through large-scale deep learning models, these enterprises achieve more accurate object recognition, path planning and real-time environment understanding. 

 For example, the Lingo-1 model developed by Wayve uses large model technology to interact with vehicles to provide self-driving vehicles with the ability to understand and execute natural language instructions, which makes future self-driving vehicles more user-friendly and intelligent. 

 At the same time, Tesla has further optimized the neural network in its latest fully autonomous driving (FSD) version to achieve longer autonomous driving in complex road conditions. 

 These enterprises improve the adaptability and flexibility of self-driving by strengthening learning and generating models. For example, the intensive learning (RL) method learns the best driving strategy by simulating the driver's decision-making process. These technologies can help vehicles better cope with complex environments, such as driving on urban streets without clear lane signs. 

 2. End-to-end self-driving solutions 

 With the maturity of technology, end-to-end deep learning solutions (such as automatic driving systems that input and output through a single neural network) are developing rapidly. Such systems rely on convolutional neural networks (CNN) and Transformer architectures to process data streams from multiple sensors such as LiDAR, cameras and GPS, thus generating real-time path planning and decision-making of vehicles. Latest models, such as MMTransformer, use multimodal processing technology to not only identify dynamic objects around vehicles, but also predict their future trajectory, so as to achieve safer decisions. These technologies can significantly improve the perception of self-driving vehicles, especially in high-density traffic environments, and help vehicles effectively avoid obstacles and make more efficient decisions. 

 3. Global cutting-edge advances of AI and autonomous driving 

 Globally, the development of self-driving in 2024 showed two obvious trends: 

 Extension of driverless taxi and self-driving services: such as Baidu Apollo in China, Waymo in the United States, and driverless services with Nvidia and its partners have begun to be piloted in many cities. Taking Nvidia as an example, they demonstrated the integration of self-driving and generating AI in CES 2024, a breakthrough that allows vehicles to achieve high-precision adaptive driving in complex urban environments. 

 Open data sharing and cooperative development: For example, TIER IV in Japan launched the Co-MLOps project, which aims to promote the further development of autonomous driving technology through open data sets and cooperation platforms. This open cooperation model can accelerate the global progress of self-driving technology. 

 4. How to improve the safety and accuracy of self-driving through large models 

 Self-driving technology still faces huge challenges in its application, the most important of which is how to improve its safety and the accuracy of decision-making. The introduction of the big model provides a new way to solve this problem. The latest generation confrontation network (GAN) technology and enhanced learning strategies are widely used to simulate real driving scenarios, so as to optimize the performance of vehicles in complex road environments. 

 For example, Nvidia's self-driving platform combines GAN and intensive learning to simulate complex urban driving scenarios and improve the vehicle's ability to deal with emergencies through data enhancement and confrontation training. This kind of technology can help self-driving vehicles make more accurate and rapid decisions in extreme weather, complex traffic flow and other scenarios. 

 5. Technology application cases of the world's leading automobile companies and AI companies 

 Tesla is still one of the world's leading enterprises in the field of self-driving. The latest FSD version in 2024 shows its further breakthrough in large model technology and improves the safety and stability of self-driving. By continuously optimizing its self-driving taxi service, Waymo has taken the lead in realizing fully self-driving operations without safety personnel in some cities in the United States. 

 6. Related technical issues and solutions 

 Question 1: How to improve the perception of autonomous driving through a large model? 

 Large models can process larger-scale multimodal data (such as images, LiDARs, GPS signals, etc.) and achieve more accurate object recognition and environmental understanding through the Transformer architecture. For example, MMTransformer uses the Attention mechanism to fuse information between different sensor data, which improves the perception accuracy. 

 Question 2: How to solve the "long tail effect" in autonomous driving? 

 The long tail effect refers to the unstable performance of self-driving vehicles in extreme or rare scenarios. Large models can generate analog data of these extreme scenes through data enhancement and GAN technology to help vehicles learn and adapt. This method has been applied on Nvidia's self-driving platform. 

 AI/Large Model - Intelligent Cabin 

 1. The evolution of the core technology of the intelligent cockpit 

 The intelligent cockpit has gradually moved from "software-defined cars" to "AI-defined cars", and the AI model has become the core force to realize this transformation. Take Xiaopeng Automobile's Tianji OS as an example. This is the first intelligent cockpit operating system fully integrated with AI technology. It applies a large 2K pure visual neural network model to accurately identify dynamic and static obstacles. The system also integrates the XPlanner model, which enables the vehicle to have humanized learning, planning and control capabilities to provide strong support for intelligent driving. 

 Question: How does Xiaopeng's AI intelligent cockpit achieve more efficient interaction? 

 Through multimodal interaction and personalization, Tianji OS can adjust cockpit settings according to the user's driving habits. In addition, the system also uses the AI model for route learning and memory to provide a personalized driving experience, which greatly improves the interactive efficiency of the smart cockpit. 

 2. Deep integration of large model technology and intelligent cockpit 

 Based on its ERNIE model, Baidu Apollo has developed a special large model technology for the intelligent cockpit, which improves the fluency and real-time of intelligent interaction in the car. The ERNIE model has stronger multimodal understanding ability and can dynamically respond to user needs through deep learning. Models such as Jihu and Cadillac have begun to apply this technology, and have demonstrated millisecond response time and multi-channel interaction functions. 

 Question: How does Baidu Apollo's ERNIE model achieve a smooth user experience in the smart cockpit? 

 THE ERNIE LARGE MODEL ENSURES THAT THE USER'S VOICE COMMANDS CAN BE QUICKLY RESPONDED THROUGH MULTIMODAL UNDERSTANDING AND ACTIVE INTERACTION. In addition, the model also supports real-time processing of multi-channel input to ensure the smooth operation of multitasking in the car. 

 3. Application of pure vision and end-to-end large models 

 The V2.0 software launched by Jikrypton Automobile in cooperation with Baidu pioneered China's first L4-level pure vision technology model, and integrated end-to-end large model intelligent driving technology. This technology not only improves the autonomous driving ability in the car, but also applies to the intelligent cockpit to provide a more immersive user experience through lip movement recognition and AI-driven interactive functions. 

 Question: How does the pure vision model improve the interaction level of the intelligent cockpit? 

 The pure vision model of Polar Krypton combines advanced AI recognition technology, which can interact through the user's lip movements, reduce dependence on voice input, and improve the immersion and interaction efficiency in the cockpit. 

 4. The future development trend of smart cockpit 

 After 2024, the development of smart cockpit will be more deeply integrated with self-driving technology. For example, Chery's Lion intelligent cockpit system uses the AI model for personalized adjustment and uses end-to-end technology to achieve human-level decision-making ability. In addition, it is expected that by 2025, Chery will realize a large-scale application of fully automatic driving. Its large model will not only be used for driving, but also further penetrate into every detail of the cockpit. 

 Question: How will the smart cockpit evolve in the future? 

 The intelligent cockpit will shift from a simple driving assistance system to a more personalized and fully automated cockpit experience. With the continuous evolution of large models, the future cockpit will be able to learn user habits independently and provide a more personalized and safer driving and interactive experience. 

 5. Cooperation and technological innovation of world-leading enterprises 

 The strategic cooperation framework between XPENG and Volkswagen Group shows that automobile manufacturers are cooperating to promote the innovation of intelligent cockpit technology. XPENG's Tianji OS not only supports intelligent driving, but will also be promoted globally, demonstrating the scalability and global potential of its big model technology. 

 Question: How will XPENG's cooperation with the public promote intelligent cockpit technology? 

 The new generation of electronic and electrical architecture developed by XPENG in cooperation with Volkswagen will integrate the most advanced AI cockpit technology to further enhance the intelligent driving and intelligent cockpit functions of vehicles, so that users around the world can enjoy a more efficient AI cockpit experience.