Edge artificial intelligence can significantly enhance the performance, safety, and efficiency of autonomous transportation systems, including autonomous vehicles (AVs) and other forms of autonomous transportation. Smart transport is a rapidly growing sector, and by enabling data to be processed locally on the network’s edge, smart transport infrastructure can improve response times, optimise cyber security, and enhance user experiences of smart transport.  

Edge AI provides enhanced privacy and security by helping to protect sensitive data generated by autonomous vehicles, as data processing and analysis occur locally on the vehicle. This reduces the risk of data breaches and unauthorised access compared to transmitting data to centralised servers for processing. Edge AI can also address the scalability of autonomous vehicles by enabling distributed processing across a fleet of autonomous vehicles, allowing them to share information and coordinate their actions efficiently without overloading centralised servers or risking cyber security.

Smart transport can be further supported by Edge AI, which enables autonomous vehicles to process sensor data, such as lidar, radar, and camera feeds, in real-time directly on board the vehicle. This allows for faster decision-making and response times, crucial for safe navigation in dynamic environments. Due to the data being processed locally on board the vehicle itself, edge AI technology reduces latency in decision-making, as there is no need to transmit data to remote servers for processing. This is particularly important for safety-critical applications, where split-second decisions can make a significant difference. Edge AI algorithms can also perform localisation and mapping tasks directly on the vehicle, using onboard sensors and data. This enables autonomous vehicles to navigate accurately without relying solely on pre-built maps or GPS signals, which may be unavailable or inaccurate in certain environments.

Decision-making and safety aspects on autonomous vehicles can be enhanced through Edge AI technology through its support of dynamic adaptation to environment changes. Edge AI enables autonomous vehicles to adapt to changing road conditions, traffic patterns, and obstacles in real-time. By analysing sensor data locally, vehicles can adjust their behaviour and trajectories instantaneously to navigate safely and efficiently. Edge AI can help autonomous vehicles adapt to potential dangers by deploying edge-based perception and object recognition. Edge AI algorithms can recognise and classify objects, such as pedestrians, cyclists, and other vehicles, directly on the vehicle itself. This enhances situational awareness and allows autonomous vehicles to react appropriately and quickly to potential hazards on the road.

Similarly to processing data locally on board the vehicle, Edge AI also allows autonomous vehicles to operate even in environments with limited or intermittent connectivity, as they can continue to process data and make decisions independently of external infrastructure. Processing data locally through Edge AI can also optimise the energy consumption of autonomous vehicles by minimising the need for communication with external servers. This energy efficiency can, thus, extend the range of electric autonomous vehicles and reduce overall energy consumption.