Your ride could be 100% autonomous in 2030, as 90.4% of cars sold then are expected to have automated capabilities of Level 1 to Level 4 models. Of this, 2.6 million will have Level 4 features, according to Yahoo Finance. Riding in a car that drives itself was once a far-fetched idea. But it’s slowly becoming a reality, as self-driving cars promise continuous operation. Think of robotaxis like Waymo, Pony.ai, and Baidu Apollo that offer round-the-clock riding services. They are revolutionizing travel in urban areas without the hindrance of human fatigue. However, there’s a challenge in developing vehicles that guarantee 24/7 duty cycles. Without humans to determine rest time, adapt to changes, or report mechanical issues, cars operating round-the-clock require systems engineered for longevity, self-management, and redundancy. This post highlights the challenges autonomous vehicle engineers face and solutions.
Continuous Hardware Failure
The frequent vibration of vehicles driving nonstop for 24 hours puts stress on braking and steering systems, suspension components, and structural mounts. Computational loading also adds pressure on vehicle parts. With vehicles operating on a 24/7 cycle, it means onboard computers process data continuously, which results in extreme heat being produced and degrading batteries, sensors, and other crucial components. That said, auto engineers are challenged to maintain hardware longevity. So, what solutions are auto manufacturers adopting? They choose industrial-grade materials, like titanium alloys, to design for advanced duty cycles.
For example, titanium wires are ideal for designing engine valves and suspension systems that encounter extreme cyclic loads because of the fatigue resistance capabilities they possess. Sensor mountings, fasteners, tethers for LiDARs, and thermal cables encounter heat, but wires made of titanium maximize their durability. Plus, they don’t increase the weight of components because of their high-strength and lightweight ratio. Note that high-grade titanium is required throughout a vehicle’s lifecycle. So, automakers do a thorough search for titanium wires near me and partner with reliable suppliers to minimize downtime. Alongside using durable materials, engineers apply modular architecture in AVs for ease of assembly, upgrades and maintenance. Implementing modularity enables auto manufacturers to compartmentalize complicated functionalities like navigation algorithms, sensor fusion, and user interface systems.
Maintaining Software Stability
The perception and control of fully automated vehicles rely on a software stack. If a minor bug or virus invades a car’s sensor architecture, it can cause catastrophic consequences. Imagine this: the brakes of a driverless car fail because the sensors have been corrupted by software. The vehicle will crash into other automobiles, buildings, and people, causing fatalities. Nonstop operations also cause memory leaks, performance degradation, and model drift. Driverless car developers borrow the best strategies from aerospace engineering to overcome these obstacles. These include over-the-air or OTA and hardware and software in the loop testing. End-to-end simulations are also adopted to reinforce software reliability and safety.
Strong Perception and Sensor Integration
For self-driving vehicles to function, they need strong perception. After all, how will they navigate in environments if they don’t understand them in real-time? To ensure autonomous cars see where they’re going, data must be integrated with multiple sensors. This includes radar, LiDAR, ultrasonic detectors, and cameras. But these sensors have limitations that present complex obstacles for engineers. LiDAR, for example, is sensitive to weather and expensive, while cameras struggle to capture images in low light. Limitations of radar include interference with other radars, excessive dependence on specialized processing, and inability to detect light.
So, how are automakers overcoming these challenges? Engineers are turning to sensor fusion algorithms that combine the strengths of various sensors to build cohesive environmental models. Kalman filtering combined with deep learning fusion is an example of advanced techniques used to improve accuracy and reliability. For enhanced environment discernment, such as identifying pedestrians, road conditions, and other vehicles, convolutional neural networks or CNNs and recurrent neural networks (RNNs) are being used. Besides using deep learning models, engineers continuously test cars in virtual environments, and fuse control systems and backup sensors for redundancy and safety.
What could be a more complex engineering task than the development of autonomous automobiles? Possibly none. Each component and design approach demands innovative solutions and intensive validation to address pressing challenges. Some practices engineers are incorporating include using high-grade materials, particularly titanium, in design to extend component lifecycles. Automakers are also performing vigorous tests and leveraging advanced sensor algorithms to boost sensor performance and software reliability.