System Trade Offs in Architectural Design of Self Driving Systems
System trade-offs are an inherent part of the architectural design of self-driving systems. These trade-offs involve making decisions that prioritize certain system characteristics over others, based on the specific requirements and constraints of the application. In the context of self-driving systems, these trade-offs often involve balancing the competing demands of safety, performance, cost, and flexibility.
One key trade-off in the architectural design of self-driving systems is the balance between sensor redundancy and cost. Self-driving vehicles rely on a variety of sensors, such as lidar, radar, and cameras, to perceive their environment and make decisions. These sensors can be expensive, and adding more sensors can significantly increase the cost of the system. However, adding redundant sensors can also improve the reliability and robustness of the system, as it allows the vehicle to continue functioning even if one or more sensors fail. Therefore, designers must carefully consider the trade-off between the benefits of added redundancy and the costs of additional sensors.
Another important trade-off in self-driving system design is the balance between centralized and decentralized processing. In a centralized system, all of the sensor data is processed in a single location, such as a central computer or server. This can provide a high level of control and coordination, as all of the processing is done in a single location. However, it can also be less flexible and more vulnerable to failure, as the entire system relies on the central processor. On the other hand, decentralized systems distribute the processing across multiple computers or processors, which can improve flexibility and resilience. However, it can also be more challenging to coordinate and control the overall system.
There are also trade-offs in the choice of hardware and software platforms for self-driving systems. Different platforms have different capabilities and constraints, and designers must choose the platforms that best meet the needs of the application. For example, using a high-performance platform may allow for faster processing and more sophisticated algorithms, but it may also be more expensive and consume more power. On the other hand, a lower-performance platform may be more cost-effective and energy-efficient, but it may not be able to support the same level of complexity.
In summary, the architectural design of self-driving systems involves a range of trade-offs that must be carefully considered in order to achieve the desired performance and capabilities. These trade-offs include the balance between sensor redundancy and cost, the choice between centralized and decentralized processing, and the selection of hardware and software platforms. Ultimately, the best design will depend on the specific requirements and constraints of the application.