Planning Decision Engineer

[Code block moved to code-block-1.md]

§ 10 · Common Pitfalls & Anti-Patterns

Anti-Pattern 1: Treating Safety as a Soft Cost

Name: The Weighted Safety Engineer

❌ BAD:

# Safety encoded as soft cost — can be traded away by efficiency gain
cost = 10.0 * safety_proximity_cost + 1.0 * speed_cost + 0.5 * comfort_cost
# A high-speed, risky trajectory can have lower total cost than a safe slow one

✅ GOOD:

# Safety is a hard constraint — infinite cost for violation
if min_clearance_to_obstacles < 0.5:   # safety radius violation
    return np.inf                      # trajectory immediately rejected
# Only feasible trajectories participate in soft cost comparison
cost = 1.0 * speed_cost + 0.5 * comfort_cost  # optimize over safe set only

Why it matters: With soft costs, the planner can trade safety margin for speed in dense traffic. This is unacceptable — safety margins must be absolute constraints.

Anti-Pattern 2: Planning Against Single-Mode Prediction

Name: The Most-Likely-Future Planner

❌ BAD:

# Only use the highest-probability prediction mode
predicted_traj = predictor.predict(agent)[0]  # mode with highest prob
plan_path_around(predicted_traj)

✅ GOOD:

# Plan against all modes above probability threshold
predicted_modes = predictor.predict_multimodal(agent, num_modes=6)
safety_violated = False
for mode in predicted_modes:
    if mode.probability > 0.05:  # consider modes with > 5% probability
        if trajectory_collision_check(ego_plan, mode.trajectory):
            safety_violated = True
            break
if safety_violated:
    ego_plan = replan_conservative()  # give way to ambiguous agent

Why it matters: A vehicle with 70% probability of going straight and 30% probability of turning left requires a plan that is safe for both cases. Optimizing only for the 70% case produces a plan that collides 30% of the time.

Anti-Pattern 3: Frenet Frame Used Beyond Its Valid Domain

Name: The Curved-Road Frenet Abuser

❌ BAD:

# Using Frenet planner on sharp curves without curvature correction
# At κ = 0.1 m⁻¹, Frenet-to-Cartesian projection has significant error
frenet_planner.plan(ego_state, target_speed=15.0)  # valid up to κ ≈ 0.05 m⁻¹

✅ GOOD:

# Check curvature before applying Frenet planner; switch to Cartesian for sharp curves
max_kappa = max(abs(kappa) for kappa in reference_path.curvature_profile)
if max_kappa > 0.05:  # 20m radius of curvature
    # Switch to Cartesian-space optimization (e.g., Apollo's open-space planner)
    plan = cartesian_space_planner.plan(ego_state, reference_path)