refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-self-driving-car-foundations
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title: Self-Driving Car Foundations
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Autonomous Driving, AV Stack, Self-Driving]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [autonomous-driving, av, perception, planning, end-to-end]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Self-Driving Car Foundations
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## 매 한 줄
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> **"매 sense → predict → plan → act, with redundancy at every layer"**. 매 modular stack (perception/prediction/planning/control) 매 dominant historically, 매 2026 trend → end-to-end neural (Tesla FSD v12, Wayve GAIA-2). 매 SAE L4 production: Waymo, Cruise(suspended), Zoox.
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## 매 핵심
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### 매 Sensor stack
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- **Camera**: 매 dense semantic, 매 RGB perception.
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- **Lidar**: 매 sparse depth (Waymo, Zoox).
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- **Radar**: 매 velocity, 매 weather-robust.
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- **GNSS+IMU**: 매 ego pose; 매 HD map alignment.
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- Tesla pure-vision: 매 cameras + neural net only.
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### 매 Modular pipeline
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1. **Perception**: 3D detection (CenterPoint, BEVFormer), segmentation, lane detection.
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2. **Prediction**: 매 multi-agent trajectory (MTR, Wayformer).
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3. **Planning**: 매 sample-based (lattice), optimization (MPC), or learned.
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4. **Control**: PID, MPC, LQR (매 lateral + longitudinal).
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### 매 End-to-end (E2E) trend
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- **Tesla FSD v12**: 매 video → controls neural net.
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- **Wayve GAIA-2**: 매 generative world model + policy.
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- **UniAD** (CVPR 2023 best paper): 매 unified perception+planning transformer.
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- 매 advantage: 매 less brittle handoff; 매 disadvantage: 매 interpretability.
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### 매 SAE levels
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- L2 (ADAS): 매 hands-on, driver responsible.
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- L3 (Mercedes Drive Pilot): 매 hands-off in conditions.
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- L4 (Waymo, Zoox): 매 ODD-bound full autonomy.
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- L5: 매 anywhere — 매 not yet achieved.
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### 매 응용
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1. Robotaxi (Waymo One, Zoox launch 2024).
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2. Trucking (Aurora, Kodiak).
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3. ADAS consumer (Tesla, Mercedes).
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4. Last-mile delivery (Nuro).
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## 💻 패턴
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### BEV perception (BEVFormer-style)
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```python
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import torch.nn as nn
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class BEVPerception(nn.Module):
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def __init__(self, n_cams=6, bev_h=200, bev_w=200):
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super().__init__()
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self.image_backbone = ResNet50()
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self.bev_queries = nn.Parameter(torch.randn(bev_h * bev_w, 256))
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self.spatial_attn = DeformableAttention()
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self.det_head = DetectionHead(num_classes=10)
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def forward(self, multi_cam_imgs, cam_intrinsics, cam_extrinsics):
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feats = [self.image_backbone(img) for img in multi_cam_imgs]
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bev = self.spatial_attn(self.bev_queries, feats, cam_intrinsics, cam_extrinsics)
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boxes_3d = self.det_head(bev)
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return boxes_3d
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```
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### Trajectory prediction (multimodal)
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```python
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class MultiModalPredictor(nn.Module):
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def __init__(self, k_modes=6):
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super().__init__()
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self.k = k_modes
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self.encoder = AgentEncoder()
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self.mode_head = nn.Linear(256, k_modes * 80) # 80 = 8s @ 10Hz * 2 (xy)
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self.score_head = nn.Linear(256, k_modes)
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def forward(self, agent_history, map_features):
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ctx = self.encoder(agent_history, map_features)
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trajs = self.mode_head(ctx).view(-1, self.k, 40, 2)
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scores = self.score_head(ctx).softmax(-1)
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return trajs, scores
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```
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### MPC planner
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```python
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import casadi as ca
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def mpc_plan(x0, ref_path, horizon=20, dt=0.1):
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opti = ca.Opti()
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x = opti.variable(4, horizon+1) # [x, y, theta, v]
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u = opti.variable(2, horizon) # [accel, steer]
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opti.subject_to(x[:, 0] == x0)
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cost = 0
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for t in range(horizon):
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# Bicycle model
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nx = x[0, t] + x[3, t] * ca.cos(x[2, t]) * dt
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ny = x[1, t] + x[3, t] * ca.sin(x[2, t]) * dt
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nt = x[2, t] + x[3, t] / 2.5 * ca.tan(u[1, t]) * dt
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nv = x[3, t] + u[0, t] * dt
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opti.subject_to(x[:, t+1] == ca.vertcat(nx, ny, nt, nv))
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cost += ca.sumsqr(x[:2, t] - ref_path[:, t]) + 0.1 * ca.sumsqr(u[:, t])
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opti.minimize(cost)
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opti.subject_to(opti.bounded(-3, u[0, :], 3))
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opti.subject_to(opti.bounded(-0.5, u[1, :], 0.5))
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opti.solver("ipopt")
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sol = opti.solve()
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return sol.value(u[:, 0])
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```
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### End-to-end policy (UniAD-style)
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```python
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class E2EDriving(nn.Module):
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def __init__(self):
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super().__init__()
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self.bev_perception = BEVPerception()
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self.predictor = MultiModalPredictor()
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self.planner = nn.TransformerDecoder(...)
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def forward(self, sensor_data, ego_state, command):
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bev = self.bev_perception(sensor_data)
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trajs = self.predictor(bev)
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plan = self.planner(query=ego_state, memory=torch.cat([bev, trajs]),
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tgt_mask=command_to_mask(command))
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return plan # 매 future ego trajectory
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```
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### Sensor fusion (Kalman-like)
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```python
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class EKF:
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def predict(self, dt):
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F = build_motion_jacobian(self.state, dt)
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self.state = motion_model(self.state, dt)
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self.P = F @ self.P @ F.T + self.Q
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def update(self, z, H, R):
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y = z - H @ self.state
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S = H @ self.P @ H.T + R
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K = self.P @ H.T @ np.linalg.inv(S)
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self.state += K @ y
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self.P = (np.eye(len(self.state)) - K @ H) @ self.P
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```
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### Safety: redundant trajectory check
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```python
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def safety_filter(planned_traj, predicted_agents, ttc_threshold=2.0):
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for agent_traj in predicted_agents:
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for t in range(len(planned_traj)):
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d = distance(planned_traj[t], agent_traj[t])
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v_rel = relative_velocity(planned_traj, agent_traj, t)
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ttc = d / max(v_rel, 0.1)
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if ttc < ttc_threshold:
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return emergency_brake_trajectory()
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return planned_traj
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Robotaxi L4 | 매 modular + HD map (Waymo pattern) |
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| Consumer ADAS | 매 vision-first E2E (Tesla FSD) |
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| Trucking highway | 매 modular + lidar (Aurora) |
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| Research SOTA | 매 E2E transformer (UniAD, GAIA-2) |
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| Safety case | 매 modular (interpretable) + ML perception |
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**기본값**: 매 production deploy 면 modular stack with E2E modules; 매 R&D 면 full E2E + world model.
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## 🔗 Graph
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- 부모: [[Robotics]] · [[Computer Vision]] · [[Reinforcement Learning]]
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- 변형: [[End-to-End Driving]]
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- 응용: [[Waymo]] · [[Tesla FSD]]
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- Adjacent: [[MPC]]
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## 🤖 LLM 활용
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**언제**: 매 AV stack architecture decision, 매 perception/prediction/planning module design.
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**언제 X**: 매 ROS-level driver/firmware code (매 different domain), 매 hardware certification (ASIL-D).
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## ❌ 안티패턴
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- **No safety driver during validation**: 매 fatal — 매 Uber 2018 case.
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- **Single point of failure**: 매 redundant sensors+compute 필수.
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- **HD map dependency only**: 매 fragile in new locations — 매 Tesla bet against, Waymo bets for.
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- **End-to-end without monitor**: 매 unverifiable — 매 always include shadow rule-based safety filter.
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## 🧪 검증 / 중복
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- Verified (Waymo Safety Report 2023, Tesla AI Day 2022/2023, UniAD CVPR 2023).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — modular vs E2E, BEVFormer, MPC, safety filter |
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