Beyond environmental data, self-driving cars require precise location tracking. A collaborative positioning module processes GPS, IMU, vehicle dynamics, sensor features, and high-precision maps to determine the vehicle’s location and destination trajectory. Using this data alongside multi-sensor fusion inputs, the decision-making module performs real-time dynamic planning. Finally, the autonomous vehicle’s operational status and information are displayed on both the human-machine interface and the background management system.

Decision-making control technology drives intelligent autonomy. Using sensor and positioning data, AI techniques like deep, machine, and reinforcement learning enable real-time path planning, obstacle identification, and environmental behavior prediction. Self-driving cars must adaptively navigate dynamic environments with pedestrians and traffic signals while balancing safety and performance. They must ensure strict traffic compliance and emergency responsiveness, alongside efficient driving strategies that reduce transit time and energy consumption.

Autonomous driving requires robust cybersecurity and privacy frameworks. Reliable security mechanisms must safeguard vehicle communications and control systems against hacking, malicious interference, or unauthorized tampering. Concurrently, strict privacy policies and technical measures are vital to protect passengers and pedestrians, ensuring the security and absolute confidentiality of the massive amounts of collected sensory data and personal location information.