自動運転における最適化：複雑性からリアルタイムエンジニアリングへ

Key Takeaways

A production-grade AV stack is best understood as a distributed dataflow graph of publish/subscribe components (often cyclic in practice due to feedback and replanning), typically implemented via middleware such as ROS 2 on top of Data Distribution Service (DDS).

Engineering an AV stack is not just writing code that follows logic; it is building a system that manages resources, time, and physics constraints simultaneously.

Optimization in perception often means context-aware prioritization: adjusting sensing, preprocessing, and inference effort to match the current Operational Design Domain (ODD).

Instead of hard-coding rules, engineers define a Cost Function (J) that the solver minimizes.

Many teams treat the compute budget itself as an engineering optimization problem: They measure execution times, allocate cores, set priorities, and tune quality of service (QoS) so the right work happens at the right time.

Introduction

Autonomous driving systems are often discussed in terms of AI capabilities or high-level ethics. However, for the software architects and engineers building these systems, the reality is a battle against latency, bandwidth, and computational constraints. This article explores the end-to-end technical architecture of an AV stack, illustrating how optimization techniques, from context-aware sensor fusion to Model Predictive Control (MPC) solvers, turn gigabytes of raw sensor data into safe control commands within millisecond-level deadlines.

The End-to-End Architecture: From Sensor to Actuation

At first glance, automated driving systems reveal formidable complexity. These systems are not simple linear pipelines; they are recursive, real-time loops of perception, prediction, planning, and control.

To understand where optimization is required, it helps to first look at the data flow. A production-grade AV stack is best understood as a distributed dataflow graph of publish/subscribe components (often cyclic in practice due to feedback and replanning), typically implemented via middleware such as ROS 2 on top of DDS (Data Distribution Service). The pipeline must ingest and process massive amounts of data from cameras, radars, LiDARs, GNSS, and IMUs every second.

Figure 1 below summarizes this end-to-end architecture, from high-rate sensor inputs through perception/localization and fusion to planning, control, and actuation, so the main data and compute flow is visible at a glance.

Figure 1: High-level AV software architecture

[Click here to expand image above to full-size]

Typical Data Throughput Volumes

LiDAR: ~0.3–2.6 million points/sec (often ~35–255 Mbps per sensor depending on configuration).

Cameras: 4K/60fps streams (full-color uncompressed video can require ~12 Gbps; Production systems typically rely on RAW formats and/or compression).

Radar: Sparse detections/tracks (typically low bandwidth, high refresh rate).

Optimizing the Perception Pipeline: Dynamic Resource Allocation

The perception layer is responsible for turning raw data into a world model. A naive approach processes every sensor at full resolution and maximum frequency. However, processing gigabytes of data every second at full fidelity would saturate the computational resources of any vehicle.

Context-Aware Sensor Prioritization

Optimization in perception often means context-aware prioritization: adjusting sensing, preprocessing, and inference effort to match the current Operational Design Domain (ODD). Stacks frequently model the computational cost of key pipeline stages and apply policies (or optimization-based controllers) that trade off accuracy, latency, and resource usage.

Highway Scenario

The Region of Interest (ROI) narrows and long-range precision is critical. Consequently, stacks often prioritize forward-looking LiDAR and long-range cameras, while reducing load from side-facing sensors via downsampling, reduced frame/scan rates, or selective ROI processing.

Urban Scenario

Peripheral coverage becomes more important for cross-traffic, vulnerable road users, and complex interactions. Stacks often prioritize wide-angle cameras and side-looking sensors, and may allocate more compute to semantic perception and tracking.

Figure 2: Dynamic Sensor Weighting Logic

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Technical Implementation: From Preprocessing to Fusion

In production AV programs, perception pipelines need this kind of flexible allocation. Many teams go beyond single-stage object lists and design pipelines that manage high-rate sensor streams with low latency, starting from early preprocessing through inference and tracking. In practice, this fusion typically involves two complementary controls. First, there are processing knobs (rate, ROI, resolution, model choice) to manage compute load. Next are fusion weights that scale measurement uncertainty (e.g., the measurement covariance, R) in tracking.

LiDAR Processing

Raw point clouds (x, y, z, intensity) are typically discretized (voxelization or pillarization) and then consumed by 3D detection networks such as VoxelNet-family approaches or PointPillars. The voxel/pillar resolution is a critical trade-off between spatial fidelity and inference latency/compute.

Radar Processing

Radar measurements (range, angle, range-rate) are often leveraged for robust velocity cues and adverse weather operation; uncertainty can be adjusted by context and clutter characteristics.

Tooling

Deployment pipelines often use inference accelerators such as TensorRT to optimize and run deep learning models on embedded GPU platforms (for example, NVIDIA Xavier-class systems; Newer generations also target Orin-class hardware). Model choices vary by stack, but standard backbones (e.g., ResNet) and detector families (e.g., YOLO-style) are widely used in computer vision alongside 3D-/BEV-specific architectures in AV.

As Ahn et al., n.d. show, combining data-parallel execution with selective GPU offload can improve end-to-end throughput and latency in perception pipelines while maintaining accuracy targets.

To visualize how this logic looks in the tracking/fusion layer, consider a context-aware weight manager. In a Kalman-filter-based tracker, sensor trust can be represented via the measurement covariance R (often per sensor and per measurement type): Higher covariance reduces the filter’s reliance on a measurement, while lower covariance increases it.

Pseudocode: Dynamic Sensor Weighting (Python)

class SensorFusionManager:

def update_weights(self, vehicle_state, environment_context):

"""

Dynamically adjusts sensor trust (covariance) based on context.

Low covariance = High Trust.

"""

# Base configuration (illustrative scalar variances / scale factors)

lidar_cov = 0.1

camera_cov = 0.2

radar_cov = 0.3

# SCENARIO: High-Speed Highway

# Trust long-range Radar/LiDAR more; Cameras may suffer motion blur

if vehicle_state.speed > 100.0: # km/h

radar_cov = 0.1 # Increase trust in Radar for velocity

camera_cov = 0.5 # Decrease trust in Camera

# SCENARIO: Urban / Congested

# Trust Cameras for semantic understanding (pedestrians, signs)

elif environment_context.type == 'URBAN_DENSE':

lidar_cov = 0.05 # Max trust in LiDAR for close-range geometry

camera_cov = 0.1 # High trust for object classification

radar_cov = 0.4 # Radar can be less reliable for some cues/associations in dense clutter

return self.kalman_filter.update_covariance(

lidar=lidar_cov,

camera=camera_cov,

radar=radar_cov

)

Trajectory Planning: The Mathematics of MPC

While perception deals with probabilities, planning deals with constraints. The planning module generates a feasible trajectory, typically parameterized over a horizon as a sequence of states and controls {xk,uk}Nk=0 , at a fixed control cadence.

It is commonly on the order of tens of milliseconds to approximately one hundred milliseconds depending on the stack and platform. Missing this deadline degrades responsiveness.

The Optimization Problem

Trajectory generation is commonly framed as a Model Predictive Control (MPC) problem. Instead of hard-coding rules, engineers define a Cost Function (J) that the solver minimizes.

J=∑N−1k=0(‖xk−xrefk‖2Q+‖uk‖2R)+‖xN−xrefN‖2P

Here {xrefk}Nk=0 denotes the reference trajectory over the prediction horizon.

Where

xk: State vector at step k (position, velocity, yaw).

uk: Control input at step k (steering angle, acceleration).

xrefk: reference state at step k (the desired position/heading/velocity at that point along the horizon), typically provided by a higher-level planner, route, or behavior module.

xrefN: terminal reference state at the end of the horizon (the reference at step N), used to encourage convergence toward the desired end-of-horizon condition

Q, R, and P: Weight matrices. By tuning Q and R and P, engineers optimize for "assertiveness" vs. "comfort".

Solving Under Constraints

The solver must find the minimum J subject to hard constraints.

Actuation and dynamics limits

||≤δmax (Steer)|α|≤αmax (Accel), plus rate limits where applicable.

Safety Corridors

The planned ego footprint must remain within the drivable region and maintain separation from obstacles (often expressed via corridor boundaries, signed-distance constraints, or convex approximations of collision geometry).

Figure 3: MPC Control Loop

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Solvers and Algorithms

In autonomous driving, MPC has been used to balance speed and comfort while reacting safely. To meet embedded deadlines, teams typically rely on warm-started solvers: QP solvers such as OSQP for convex MPC formulations, and nonlinear programming solvers (e.g., Ipopt) or real-time NMPC toolchains for nonlinear formulations.

Research by Allamaa et al. 2024 illustrates how advanced MPC formulations and hybrid optimization techniques provide safe, agile decision-making.Earlier work by Zhang, Rossi, and Pavone 2015 provides a broader example of MPC as receding-horizon decision-making in autonomous mobility systems at the fleet coordination level, rather than ego-vehicle trajectory control. Additionally, Arrigoni, Braghin, and Cheli 2021 research explores an alternative approach in which an NMPC trajectory planner is solved using a genetic algorithm strategy. In production (often C++) implementations, the optimization loop must be highly efficient, predictable, and instrumented for worst-case performance.

Pseudocode: MPC Cost Function (C++)

// Simplified MPC Cost Calculation Loop

double calculate_cost(const std::vector<State>& preds,

const Trajectory& ref_traj,

const std::vector<Control>& u_seq) {

double total_cost = 0.0;

// Weights for tuning behavior (Comfort vs. Tracking)

const double W_POS = 10.0; // Penalty for position error

const double W_JERK = 50.0; // High penalty for jerky steering (Comfort/smoothness Δsteer)

const double W_VEL = 1.0; // Penalty for speed deviation

for (int t = 0; t < HORIZON_N; ++t) {

// 1. State Deviation Cost (Tracking Accuracy)

const double pos_error = (preds[t].x - ref_traj[t].x);

const double vel_error = (preds[t].v - ref_traj[t].v);

total_cost += W_POS * (pos_error * pos_error);

total_cost += W_VEL * (vel_error * vel_error);

// 2. Control Input Cost (Passenger Comfort)

// Penalize large changes in steering (delta_delta)

if (t > 0) {

const double steering_delta_penalty = u_seq[t].steer - u_seq[t-1].steer;

total_cost += W_JERK * (steering_delta_penalty * steering_delta_penalty);

}

}

return total_cost;

}

Real-Time Compute Budget and Middleware

An AV stack is a "busy ecosystem." Localization, perception, prediction, and control all run in parallel, competing for the same CPU and GPU resources. If perception takes too long to process an image, the planning module might miss its update window.

Deterministic Scheduling

To prevent this problem, many teams treat the compute budget itself as an engineering optimization problem: They measure execution times, allocate cores, set priorities, and tune QoS so the right work happens at the right time. Worst-Case Execution Time (WCET): Each node has a measured (or conservatively estimated) WCET and an explicit deadline budget. Deterministic Scheduling Policies: Real-time scheduling is enforced either via an RTOS in safety-/control-critical domains or via real-time scheduling configurations on general-purpose operating systems. Fixed-priority preemptive scheduling is common; protocols such as priority inheritance help bound blocking on shared resources and protect deadline-critical tasks.

In practice, these budgets are multi-rate: high-level planning often runs at ~10-20 Hz (50-100 ms), while low-level control loops can run at ~50-100 Hz (10-20 ms) on dedicated controllers; exact rates depend on platform and safety architecture.

Module

Allocated Time

Hardware Target

Function

Sensor Acquisition

0 - 10 ms

FPGA / NIC

Timestamping & Packetization

Pre-Processing

10 - 25 ms

GPU (CUDA)

PointCloud filtering, Image resizing

Perception Inference

25 - 55 ms

NPU / GPU

CNN inference (YOLO/PointPillars)

Fusion & Tracking

55 - 65 ms

CPU

Kalman Filtering, Object ID association

Prediction & Plan

65 - 85 ms

CPU

Intent prediction, trajectory optimization (e.g., MPC)

Safety Check

85 - 90 ms

Safety Core

Rule checks, constraint validation, fallback triggering

Control & Actuation

90 - 100 ms

ECU

CAN bus command transmission

Table 1: Example Latency Budget for a 100ms Control Cycle (Illustrative)

The importance of this rigor is emphasized by Sun et al. 2023, who propose an integrated framework to analyze end-to-end latency in multi-rate AV software stacks, ensuring that critical task chains meet their deadlines.

Debugging and Explainability: The Data Layer

Optimization makes systems smarter, but also harder to debug. When an MPC solver chooses a path, it is based on the convergence of a cost function, not a simple "if-then" statement.

To solve this issue, teams engineer robust logging pipelines. They record the specific constraints considered, the trade-offs balanced, and the route chosen.

Data formats

For time-synchronized robotics data, common choices include container formats such as MCAP (widely used for robotics log capture and replay) and dataset-oriented formats such as HDF5, depending on the analysis workflow and storage constraints.

Schemas

Many teams define strict, versioned schemas using Protocol Buffers or FlatBuffers to ensure type safety, forward/backward compatibility, and reliable tooling across components.

Example: Perception Object Schema (Protobuf)

message DetectedObject {

// Unique tracking ID for temporal consistency

uint32 track_id = 1;

// Object Classification

enum Type { UNKNOWN=0; PEDESTRIAN=1; VEHICLE=2; CYCLIST=3; }

Type type = 2;

// State Vector [x, y, z, vx, vy, vz, yaw]

repeated float state = 3 [packed=true];

// 3D Bounding Box Dimensions

Vector3 dimensions = 4;

// Covariance Matrix (flattened 7x7) for Sensor Fusion trust levels

repeated float covariance = 5 [packed=true];

}

This data forms the backbone of explainability. Suresh Kolekar et al. 2022 show that visualization tools like Grad-CAM give people a window into how AI models see the world. That kind of insight doesn’t just help with safety checks, it supports transparency when communicating model behavior.

Final Thoughts

Optimization is not just a mathematical method for autonomous vehicles; it is the glue that holds the entire system together. It shapes how perception workloads are scheduled and accelerated (including GPU kernel- and graph-level optimizations where applicable), how constrained optimization problems are formulated and solved in planning, and how real-time scheduling policies and middleware QoS are configured to meet latency and safety requirements.

For the software engineer, the takeaway is clear: Engineering an AV stack is not just writing code that follows logic; it is building a system that manages resources, time, and physics constraints simultaneously. As the industry pushes the boundaries of autonomy, the ability to optimize these trade-offs will remain a defining skill.

About the Author

Avraam Tolmidis

Avraam Tolmidis is a Senior Manager, Software Development Engineering at Expedia Group. He has led software engineering teams across both industry and academia, with experience spanning integrity systems, fintech platforms, automotive software, and applied AI. Previously, Avraam was an engineering manager at Meta, where he led teams working on large-scale Integrity systems. Before Meta, he managed engineering teams at Backbase in Amsterdam, building core banking platforms for retail and business clients, and held technical roles in the automotive sector at Continental, ZF, and Aptiv. He holds a Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki, with research focused on optimization and collaborative robotics, and later work applying AI to urban traffic management.

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