In the realm of autonomous driving, when the topic of computing power arises, many immediately assume that "more is always better." The allure of faster chips and larger computing power reserves suggests that vehicles can perceive their surroundings more clearly, make decisions more swiftly, and operate with greater safety. However, this belief oversimplifies the situation. While computing power is undeniably crucial for autonomous vehicles, it is not a panacea. The question then arises: Where does computing power truly offer advantages, and where does it become a bottleneck or an unnecessary expense?

Why is computing power so highly regarded in autonomous driving?

The core functions of an autonomous driving system can be broadly categorized into perception, localization, decision-making (planning), control, and redundancy/safety verification. Perception entails swiftly and accurately transforming data from sensors like cameras, radars, LiDAR, and ultrasonic sensors into a comprehensive understanding of the surrounding environment (identifying obstacles, their locations, speeds, and potential intentions). Localization situates the vehicle within a high-precision map or relative coordinate system. Decision-making involves calculating the next maneuvers within hundreds of milliseconds or even less. Control translates these decisions into throttle, brake, and steering inputs. Many of these processes involve "parallel + complex operator combinations," with tasks such as deep neural networks, point cloud processing, semantic segmentation, trajectory prediction, and model predictive control (MPC) all demanding substantial computing power. Greater computing power facilitates more intricate models, higher input resolutions, faster inference, and the ability to run more redundant detection and self-checks, theoretically enhancing overall capability and safety redundancy. For instance, high-performance System-on-Chips (SoCs) tailored for vehicles (such as certain Orin series or similar platforms) aim to integrate more AI inference capabilities onboard to support more sophisticated perception and fusion algorithms.

Numerous manufacturers view computing power as a "safety net," where higher Tera Operations Per Second (TOPS) enable the deployment of larger and more precise networks simultaneously or facilitate online backtracking, redundant parallel inference, and more comprehensive health check logic when required, thereby reducing the likelihood of global system failure due to single-point faults. This is why some dedicated automotive SoC manufacturers highlight the ratio of "increasing computing power under limited power consumption," marketing the ability to accomplish more with the same power consumption as a key selling point.

Is Greater Computing Power Always Preferable?

For autonomous vehicles, enhanced computing power can improve resolution and model capacity, making detail detection more reliable and potentially reducing false negatives and positives over the long term. It also boosts low-latency parallel processing capabilities, simplifying complex multi-sensor fusion, which is particularly vital in challenging scenarios (urban intersections, densely populated pedestrian zones). Greater computing power also enables more robust redundancy mechanisms, such as cross-validating results with multiple models or employing backup models to degrade gracefully in case of anomalies, thereby enhancing "fail-operational" capabilities.

However, superior computing power does not necessarily equate to stronger autonomous driving capabilities. Increases in computing power do not translate proportionally into linear performance improvements. Often, refinements in algorithms, architectural optimizations, enhancements in data quality, and improvements in labeling strategies yield better returns than simply scaling up model size. In essence, computing power acts as an amplifier, but the quality of the output depends on the algorithms fed into it. Increasing computing power also leads to higher energy consumption and more complex thermal design requirements, posing significant engineering challenges in vehicles. Moreover, when inference latency is no longer a bottleneck, continuously stacking computing power results in diminishing returns in system performance. Excessive investment in computing power may only marginally improve models on extreme metrics while significantly increasing costs, power consumption, and validation burdens. Larger models and more complex logic also exacerbate software complexity and interpretability, raising safety verification and compliance costs. In the safety-critical automotive domain, this is no trivial matter—the more complex the reasoning chain, the harder it is to cover all boundary conditions and perform formal proofs or comprehensive testing.

What Are the Costs of Increasing Computing Power?

Deploying high computing power in vehicles entails far more than just the cost of the chips themselves. High-performance SoCs under heavy loads can consume tens to hundreds of watts of power, which ultimately converts into heat that must be managed by the vehicle's cooling system, additional heat sinks, or airflow channels. Heat not only affects sustained chip performance (thermal throttling can prevent peak computing power from being maintained continuously) but also impacts long-term reliability. Maintaining peak computing power is particularly costly in enclosed environments or high-temperature conditions. Hardware suppliers and automakers have implemented numerous designs to address this, such as dynamically adjusting power consumption based on modes, designing software-hardware collaboration (collaborative) energy-saving modes, or incorporating dedicated accelerators (e.g., compressed sensing, INT8 inference units) at the SoC level to achieve higher energy efficiency.

Increases in computing power also directly affect the vehicle's range, severely impacting driving range for electric vehicles. Running stronger computing power onboard significantly raises additional energy consumption and carbon emissions (more pronounced at scale), which must be considered when evaluating computing power.

High-end automotive-grade SoCs are expensive and typically require automotive-grade certification and long-term supply guarantees, increasing design and per-unit manufacturing costs. Even when some automakers design their own dedicated chips, balancing performance, cost, and power consumption imposes strict limits on target power consumption, thermal management, and physical size (early designs of some in-vehicle FSD computers included goals like "must stay below a certain power threshold to fit inside the vehicle"). These constraints directly affect whether higher computing power solutions can be adopted in mass production.

Increases in computing power also impact thermal management and coordination with other vehicle subsystems. Vehicles are not data centers, and heat cannot be easily dissipated. Cooling designs take up space, affect vehicle layout, and may even reduce trunk volume. Vehicle cooling is often coupled with air conditioning and battery thermal management systems, leading to difficult trade-offs in extreme driving scenarios, such as when computing power is high but thermally constrained, forcing the system to throttle down and fail to achieve intended performance.

How to Choose Among Computing Power, Energy Consumption, Cost, and Safety?

Given that computing power has both benefits and drawbacks, the choice should not blindly pursue "maximization." One approach is to utilize heterogeneous computing power and dedicated accelerators. Combining general-purpose CPUs/GPUs with specialized AI accelerators, vision processing units (VPUs), or dedicated matrix multipliers allows common inference tasks to be handled by low-power specialized units while reserving general-purpose units for rare but complex tasks, improving overall efficiency. Many automotive-grade SoCs adopt this heterogeneous architecture to boost effective computing power within power budgets. Manufacturers like Mobileye emphasize "achieving efficient computing power for ADAS/AV under very limited power consumption" in their SoC designs, reflecting this approach.

Another strategy is model compression and quantization. Quantizing floating-point models to INT8 or even lower bit widths can significantly reduce computing power demands and energy consumption while maintaining acceptable accuracy. In many practical projects, model compression, distillation, and structured pruning are preferred methods for improving inference efficiency over simply upgrading to larger chips.

Additionally, systems can be divided into different operational levels (e.g., primary perception link, secondary redundancy link, offline recording/playback link) with dynamic allocation of computing power based on vehicle state. For example, reducing the frequency of certain high-frequency but low-reward detections during steady highway cruising and temporarily increasing computing power for redundancy verification in complex driving scenarios. Such "on-demand allocation" strategies offer significant energy and durability advantages in real-world conditions. Platforms like NVIDIA's also provide rich power and performance management features to facilitate such fine-grained control.

Theoretically, some computing power demands could be offloaded to the cloud, but this approach must be used cautiously. Real-time decision-making in autonomous vehicles imposes strict requirements on latency and availability, and the risks of cloud-to-vehicle round-trip delays and network unavailability necessitate keeping critical paths onboard. In practice, the cloud is typically used for training, offline auditing, and non-critical remote services, while millisecond-level response logic remains on the vehicle.

Greater computing power often accompanies more complex model structures and operational modes, significantly increasing the number and complexity of test scenarios, replay data, and safety verification. In the automotive domain, safety verification involves more than just running models in a few cities; compliance, regression testing, and edge-case coverage grow non-linearly with complexity. Thus, greater computing power may mean exponentially increasing validation workloads, leading to soaring time and costs. Incorporating these costs into Return on Investment (ROI) calculations enables more rational decisions about whether to "double computing power again."

Final Thoughts

Computing power is merely a tool for achieving autonomous driving goals, not the goal itself. While greater computing power unlocks technical possibilities and strengthens models and systems, it also brings multidimensional costs in power consumption, thermal management, cost, and validation. A rational approach is to first clarify the operational design domain (ODD) and functional definitions, then balance computing power, algorithms, thermal management, cost, and validation capabilities at a system level. Only by doing so can computing power be effectively utilized to ensure safety and mass-producibility, rather than blindly stacking computing power for "technical hype."

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