Challenges and strategies for end-to-end autonomous driving

Although end-to-end autonomous driving technology has great potential, it still faces many challenges, such as explainability, real-time and reliability, data privacy and security, technical barriers and costs, data collection, integration and engineering implementation. In response, start-ups can adopt data enhancement, synthetic data, fleet learning, key annotation, lightweight models, effective evaluation and gradual iteration to gradually build their own end-to-end systems and continuously optimize and improve them.