第15章 生成式世界模型（Generative World Models） 1.3 评估指标体系

目录1.3 评估指标体系1.3.1 视觉质量指标1.3.2 可控性与规划能力评估第二部分续结构化伪代码算法13视频质量评估指标体系对应1.3.1算法14可控性与规划能力评估对应1.3.21.3 评估指标体系1.3.1 视觉质量指标Fréchet Video DistanceFVD与Fréchet Inception DistanceFID的适用边界需根据评估对象的时空特性严格区分。FID基于Inception-V3网络提取单帧特征计算生成样本与真实分布的Wasserstein-2距离适用于静态图像质量评估dFID​∥μreal​−μgen​∥2Tr(Σreal​Σgen​−2Σreal​Σgen​​)然而FID无法捕捉帧间时序依赖与动态一致性。FVD通过Inflated 3D ConvNetI3D提取时空联合特征扩展特征空间至视频 clip 级别对长程时序扭曲具有更高敏感性。FVD与FID的适用边界在于当评估目标涉及物体运动连续性、相机姿态变化或场景动态演化时FVD成为必需指标而FID仍适用于单帧保真度验证与生成稳定性初筛。时序一致性度量超越像素级相似性关注感知层面的帧间连贯性。LPIPS-Temporal扩展Learned Perceptual Image Patch Similarity至视频域通过预训练视觉网络AlexNet/VGG提取相邻帧深层特征计算加权特征距离dLPIPS-T​l∑​Hl​Wl​1​h,w∑​∥wl​⊙(fl​(vt​)−fl​(vt−1​))∥2光流一致性度量则通过估计光流场 Ft→t1​ 与生成视频的实际帧间位移对比评估运动连续性。一致性分数定义为光流重建误差与结构相似性指数SSIM的联合函数对抖动、突兀跳变与运动模糊提供量化检测。物理合理性检测构建于对象 permanence永恒性原则与几何约束之上。对象 permanence 验证要求模型在遮挡、视角变化或光照扰动下维持物体存在一致性通过实例分割跟踪Instance Segmentation Tracking计算身份保持率ID Preservation Rate。碰撞检测通过率引入3D边界框体积交叉检测结合时间维度构建4D时空占据网格统计违反物理穿透约束的帧比例。物理合理性指标弥补了感知质量与物理可实现性之间的鸿沟确保生成内容不仅视觉可信且符合世界模型的因果约束。1.3.2 可控性与规划能力评估轨迹跟随精度Trajectory Following Accuracy量化生成视频或规划路径与指令条件的对齐度。对于条件生成任务平均位移误差Average Displacement Error, ADE与最终位移误差Final Displacement Error, FDE构成核心指标ADET1​t1∑T​∥(xt​,yt​)−(x^t​,y^​t​)∥,FDE∥(xT​,yT​)−(x^T​,y^​T​)∥在概率性多模态生成场景下最小ADEminADE与Oracle误差计算最佳匹配假设与真实轨迹的偏差反映模型对多模态驾驶意图的覆盖能力。轨迹跟随精度不仅评估位置一致性亦需考察速度曲线平滑度、加速度连续性及航向角稳定性通过动态时间规整Dynamic Time Warping, DTW对齐不同时间尺度的轨迹模式。开环规划性能评估在NAVSIM与nuScenes基准上呈现差异化侧重。nuScenes采用L2误差与碰撞率作为核心指标关注短程3秒规划精度NAVSIM则引入专家驾驶不可区分性Human Expert Indistinguishability与可交互安全性Interactive Safety通过对抗性场景构建评估长程8秒规划鲁棒性。规划指标对比揭示开环评估易受分布偏移影响需结合闭环仿真Closed-Loop Simulation验证策略在实际交互中的稳定性采用世界模型预测的闭环评估Closed-Loop Evaluation with World Model Prediction成为前沿趋势。交通知识理解评估通过场景化规则验证实现。信号灯识别准确率测试模型对红绿状态、黄灯过渡及箭头信号语的响应正确性在包含遮挡、光照干扰与极端天气的测试子集上计算鲁棒精度。让行规则遵守率Traffic Rule Compliance Rate构建层次化规则本体涵盖优先权规则Right-of-Way、速度限制遵守、车道保持与禁止区域侵入检测。合规性评估采用基于规则的自动验证器与人工审核相结合生成可解释违规报告定位模型在交通法规理解上的结构性缺陷。第二部分续结构化伪代码算法13视频质量评估指标体系对应1.3.1pascal复制algorithm Video Quality Evaluation Suite Input: generated video clips V_gen {v_1, v_2, ..., v_N} reference video clips V_ref {v_1, v_2, ..., v_N} I3D feature extractor F_I3D Inception-V3 feature extractor F_inc optical flow estimator Φ_flow object detector D_obj Output: quality_metrics {FVD, FID, LPIPS-T, flow_consistency, physics_score} ▷ Fréchet Distance Computation procedure ComputeFréchetDistance(features_real, features_gen) μ_real ← mean(features_real, axis0) μ_gen ← mean(features_gen, axis0) Σ_real ← cov(features_real) Σ_gen ← cov(features_gen) diff ← μ_real - μ_gen covmean ← sqrtm(Σ_real · Σ_gen) ▷ Matrix square root frechet_dist ← ||diff||² Tr(Σ_real Σ_gen - 2·covmean) return frechet_dist end ▷ FID: Frame-level Fréchet Inception Distance procedure ComputeFID(V_gen, V_ref) features_real ← ∅ features_gen ← ∅ for each v in V_ref do for each frame f in sample(v, k16) do ▷ Sample 16 frames per video feat ← F_inc(f) features_real ← features_real ∪ {feat} end end for each v in V_gen do for each frame f in sample(v, k16) do feat ← F_inc(f) features_gen ← features_gen ∪ {feat} end end fid ← ComputeFréchetDistance(features_real, features_gen) return fid end ▷ FVD: Video-level Fréchet Video Distance procedure ComputeFVD(V_gen, V_ref, clip_length16) features_real ← ∅ features_gen ← ∅ for each v in V_ref do clips ← extract_non_overlapping_clips(v, lengthclip_length) for each clip c in clips do feat ← F_I3D(c) ▷ 3D CNN extracts spatiotemporal features features_real ← features_real ∪ {feat} end end for each v in V_gen do clips ← extract_non_overlapping_clips(v, lengthclip_length) for each clip c in clips do feat ← F_I3D(c) features_gen ← features_gen ∪ {feat} end end fvd ← ComputeFréchetDistance(features_real, features_gen) return fvd end ▷ LPIPS-Temporal: Perceptual Temporal Consistency procedure ComputeLPIPS-T(V_gen, layers{conv_1, conv_3, conv_5}) lpips_scores ← [] for each v in V_gen do frame_distances ← [] for t ← 1 to length(v)-1 do f_t ← v[t] f_t1 ← v[t1] perceptual_dist ← 0 for each layer l in layers do feat_t ← extract_features(f_t, networkVGG, layerl) feat_t1 ← extract_features(f_t1, networkVGG, layerl) ▷ Normalize spatial dimensions feat_t ← normalize_spatial(feat_t) feat_t1 ← normalize_spatial(feat_t1) ▷ Weighted distance (weights from network training) w_l ← get_layer_weight(l) dist_l ← mean(|feat_t - feat_t1|²) perceptual_dist ← perceptual_dist w_l · dist_l end frame_distances ← frame_distances ∪ {perceptual_dist} end lpips_scores ← lpips_scores ∪ {mean(frame_distances)} end return mean(lpips_scores) end ▷ Optical Flow Consistency procedure ComputeFlowConsistency(V_gen, threshold3.0) consistency_scores ← [] for each v in V_gen do warping_errors ← [] for t ← 1 to length(v)-1 do ▷ Estimate optical flow between consecutive frames flow_t→t1 ← Φ_flow(v[t], v[t1]) ▷ Warp frame t to t1 using estimated flow v_t_warped ← warp_image(v[t], flow_t→t1) ▷ Compute reconstruction error reconstruction_error ← ||v[t1] - v_t_warped||_1 ▷ Photometric consistency check ssim_score ← SSIM(v[t1], v_t_warped) ▷ Combined consistency metric consistency ← α · (1 - reconstruction_error/max_error) (1-α) · ssim_score warping_errors ← warping_errors ∪ {consistency} end consistency_scores ← consistency_scores ∪ {mean(warping_errors)} end return mean(consistency_scores) end ▷ Physical Reasonableness: Object Permanence Collision Detection procedure ComputePhysicsScore(V_gen, collision_threshold0.3) object_permanence_scores ← [] collision_rates ← [] for each v in V_gen do ▷ Object tracking across frames tracks ← ∅ for t ← 1 to length(v) do detections ← D_obj(v[t]) ▷ 3D bounding boxes [x, y, z, w, h, l, θ] ▷ Match with existing tracks using IoU and appearance for each track in tracks do if track.last_seen t - 5 then ▷ Track lost for 5 frames if track.id_preserved then object_permanence_scores ← object_permanence_scores ∪ {track.persistence_score} end remove track from tracks end end ▷ Update or create tracks for each det in detections do matched ← False for each track in tracks do if IoU3D(det, track.predict_location(t)) 0.5 then track.update(det, t) matched ← True break end end if not matched then tracks ← tracks ∪ {new_track(det, t)} end end end ▷ Collision detection in 4D spacetime collisions ← 0 total_intersections ← 0 for t ← 1 to length(v) do objects_at_t ← get_objects_at_time(tracks, t) for each pair (obj_i, obj_j) in combinations(objects_at_t, 2) do if obj_i.id ≠ obj_j.id then ▷ Check 3D bounding box intersection iou_3d ← compute_3d_iou(obj_i.box, obj_j.box) if iou_3d collision_threshold then ▷ Verify if physically plausible (e.g., same lane, appropriate distance) if not physically_plausible(obj_i, obj_j) then collisions ← collisions 1 end end total_intersections ← total_intersections 1 end end end collision_rate ← collisions / max(total_intersections, 1) collision_rates ← collision_rates ∪ {collision_rate} end physics_score ← { object_permanence: mean(object_permanence_scores), collision_rate: mean(collision_rates), pass_rate: 1.0 - mean(collision_rates) } return physics_score end ▷ Main Evaluation Pipeline procedure EvaluateVideoQuality(V_gen, V_ref) metrics ← {} ▷ Compute distribution-level metrics metrics.FID ← ComputeFID(V_gen, V_ref) metrics.FVD ← ComputeFVD(V_gen, V_ref) ▷ Compute temporal consistency metrics.LPIPS_T ← ComputeLPIPS-T(V_gen) metrics.flow_consistency ← ComputeFlowConsistency(V_gen) ▷ Compute physical plausibility metrics.physics ← ComputePhysicsScore(V_gen) return metrics end算法14可控性与规划能力评估对应1.3.2pascal复制algorithm Planning and Controllability Evaluation Input: predicted trajectories T_pred {τ_1, τ_2, ..., τ_N} ground truth trajectories T_gt {τ_1, τ_2, ..., τ_N} scene contexts S {s_1, s_2, ..., s_N} ▷ HDMap, traffic lights, other agents evaluation_mode ∈ {open_loop, closed_loop} benchmark ∈ {nuScenes, NAVSIM} Output: planning_metrics {ADE, FDE, minADE, compliance_rate, expert_similarity} ▷ Trajectory Following Accuracy: Displacement Errors procedure ComputeDisplacementErrors(T_pred, T_gt, K_modes5) ades ← [] fdes ← [] minades ← [] minfdes ← [] for each (τ_pred_set, τ_gt) in zip(T_pred, T_gt) do ▷ τ_pred_set contains K modal predictions [K, T, 2] (x,y coordinates) if dim(τ_pred_set) 2 then ▷ Single mode prediction τ_pred_set ← {τ_pred_set} end mode_errors ← [] for each mode_k in τ_pred_set do ▷ Resample to same time grid if necessary mode_k ← interpolate_trajectory(mode_k, target_timestepslength(τ_gt)) ▷ Compute per-timestep displacement displacements ← [] for t ← 1 to length(τ_gt) do dist ← ||mode_k[t] - τ_gt[t]||₂ ▷ Euclidean distance displacements ← displacements ∪ {dist} end ade_mode ← mean(displacements) fde_mode ← displacements[length(displacements)] ▷ Final displacement mode_errors ← mode_errors ∪ {(ade_mode, fde_mode, k)} end ▷ Standard ADE/FDE (average over modes or best-of-K) if K_modes 1 then ades ← ades ∪ {mode_errors[1].ade} fdes ← fdes ∪ {mode_errors[1].fde} else ▷ minADE: best mode matches ground truth best_ade ← min({e.ade for e in mode_errors}) best_fde ← min({e.fde for e in mode_errors}) minades ← minades ∪ {best_ade} minfdes ← minfdes ∪ {best_fde} ▷ Average over all modes (pessimistic) avg_ade ← mean({e.ade for e in mode_errors}) avg_fde ← mean({e.fde for e in mode_errors}) ades ← ades ∪ {avg_ade} fdes ← fdes ∪ {avg_fde} end end return { ADE: mean(ades), FDE: mean(fdes), minADE: mean(minades) if non_empty(minades) else null, minFDE: mean(minfdes) if non_empty(minfdes) else null } end ▷ Dynamic Time Warping for Trajectory Shape Similarity procedure ComputeDTWSimilarity(τ_pred, τ_gt, max_warping_window10) ▷ DTW finds optimal alignment between two time series of different lengths/speeds n ← length(τ_pred) m ← length(τ_gt) dtw_matrix ← zeros(n1, m1) dtw_matrix[0, :] ← infinity dtw_matrix[:, 0] ← infinity dtw_matrix[0, 0] ← 0 for i ← 1 to n do for j ← max(1, i-max_warping_window) to min(m, imax_warping_window) do cost ← ||τ_pred[i] - τ_gt[j]||₂² dtw_matrix[i,j] ← cost min( dtw_matrix[i-1, j], ▷ Insertion dtw_matrix[i, j-1], ▷ Deletion dtw_matrix[i-1, j-1] ▷ Match ) end end normalized_dtw ← dtw_matrix[n, m] / max(n, m) return normalized_dtw end ▷ Open-Loop Planning Benchmark: nuScenes vs NAVSIM procedure EvaluateOpenLoop(T_pred, T_gt, S, benchmark) metrics ← {} if benchmark nuScenes then ▷ nuScenes: Short-term (3s) accuracy and collision rate metrics.displacement ← ComputeDisplacementErrors(T_pred, T_gt) ▷ Collision rate with other agents (using ground truth future) collision_count ← 0 for each (τ_pred, s) in zip(T_pred, S) do future_agents ← s.future_agent_boxes ▷ Ground truth future for t ← 1 to length(τ_pred) do ego_box ← transform_to_box(τ_pred[t], s.ego_dimensions) for each agent_box in future_agents[t] do if overlap_2d(ego_box, agent_box) then collision_count ← collision_count 1 break end end end end metrics.collision_rate ← collision_count / (length(T_pred) · length(T_pred[1])) else if benchmark NAVSIM then ▷ NAVSIM: Long-term (8s) and expert indistinguishability metrics.displacement ← ComputeDisplacementErrors(T_pred, T_gt) ▷ PCC (Progressive Comfort Constraint) - comfort metrics comfort_score ← 0 for each τ_pred in T_pred do jerks ← compute_jerk_profile(τ_pred) accelerations ← compute_acceleration_profile(τ_pred) comfort_score ← comfort_score (1.0 if max(|jerks|) 5.0 and max(|accelerations|) 3.0 else 0.0) end metrics.comfort_rate ← comfort_score / length(T_pred) ▷ Expert similarity (using pre-trained driving behavior encoder) expert_similarity ← 0 for each (τ_pred, τ_gt) in zip(T_pred, T_gt) do pred_embedding ← behavior_encoder(τ_pred) gt_embedding ← behavior_encoder(τ_gt) similarity ← cosine_similarity(pred_embedding, gt_embedding) expert_similarity ← expert_similarity similarity end metrics.expert_similarity ← expert_similarity / length(T_pred) end return metrics end ▷ Traffic Rule Compliance Assessment procedure EvaluateTrafficCompliance(T_pred, S) compliance_rates ← {} total_violations ← 0 total_checks ← 0 for each (τ_pred, s) in zip(T_pred, S) do violations ← { red_light: 0, stop_sign: 0, right_of_way: 0, speed_limit: 0, lane_invasion: 0 } for t ← 1 to length(τ_pred) do state ← τ_pred[t] ▷ Traffic Light Compliance traffic_lights ← s.traffic_lights_at(state.position) for each light in traffic_lights do if light.state RED and vehicle_moving_towards(state, light.stop_line) then if distance_to_stop_line 5m and state.velocity 0.5 then violations.red_light ← violations.red_light 1 end end end ▷ Speed Limit Compliance speed_limit ← s.speed_limit_at(state.position) if state.velocity speed_limit 3.0 then ▷ 3 m/s tolerance violations.speed_limit ← violations.speed_limit 1 end ▷ Lane Keeping current_lane ← s.lane_at(state.position) if not point_within_lane_bounds(state.position, current_lane.bounds, tolerance0.5m) then violations.lane_invasion ← violations.lane_invasion 1 end ▷ Right of Way at Intersections if s.is_intersection(state.position) then priority ← determine_priority(s, state, τ_pred, t) if priority YIELD and not state.yielding then violations.right_of_way ← violations.right_of_way 1 end end total_checks ← total_checks 5 ▷ 5 rule categories checked per timestep end for each rule in violations do if compliance_rates[rule] not defined then compliance_rates[rule] ← [] end compliance_rates[rule] ← compliance_rates[rule] ∪ {1.0 - (violations[rule] / length(τ_pred))} end end ▷ Aggregate compliance statistics final_rates ← {} for each rule in compliance_rates do final_rates[rule] ← mean(compliance_rates[rule]) end final_rates.overall ← mean({final_rates[r] for r in final_rates}) return final_rates end ▷ Closed-Loop Evaluation with World Model procedure EvaluateClosedLoop(policy_model, world_model, initial_scenarios, num_rollouts100) closed_loop_metrics ← { success_rate: 0, collision_free_rate: 0, progress: 0, comfort: 0 } for each scenario in sample(initial_scenarios, num_rollouts) do current_state ← scenario.initial_state trajectory ← [current_state] collision ← False progress ← 0 for step ← 1 to scenario.max_steps do ▷ Get action from policy action ← policy_model(current_state, scenario.goal) ▷ Predict next state using world model (diffusion-based) next_state ← world_model.predict(current_state, action) ▷ Check for collision in predicted state if check_collision(next_state, scenario.other_agents_predicted) then collision ← true break end trajectory ← trajectory ∪ {next_state} current_state ← next_state progress ← progress distance_moved(current_state, trajectory[step-1]) end if not collision and distance_to_goal(current_state, scenario.goal) 2.0 then closed_loop_metrics.success_rate ← closed_loop_metrics.success_rate 1 end if not collision then closed_loop_metrics.collision_free_rate ← closed_loop_metrics.collision_free_rate 1 end closed_loop_metrics.progress ← closed_loop_metrics.progress (progress / scenario.optimal_progress) ▷ Comfort: jerk and lateral acceleration if length(trajectory) 2 then lat_acc ← compute_max_lateral_acceleration(trajectory) jerk ← compute_max_jerk(trajectory) comfort ← 1.0 if lat_acc 3.0 and jerk 5.0 else 0.0 closed_loop_metrics.comfort ← closed_loop_metrics.comfort comfort end end ▷ Normalize by number of rollouts for each key in closed_loop_metrics do closed_loop_metrics[key] ← closed_loop_metrics[key] / num_rollouts end return closed_loop_metrics end ▷ Main Evaluation Pipeline procedure EvaluatePlanningSystem(T_pred, T_gt, S, config) results ← {} ▷ Trajectory following accuracy results.trajectory_accuracy ← ComputeDisplacementErrors(T_pred, T_gt, K_modesconfig.K) ▷ Open-loop benchmark performance results.open_loop ← EvaluateOpenLoop(T_pred, T_gt, S, config.benchmark) ▷ Traffic rule compliance results.compliance ← EvaluateTrafficCompliance(T_pred, S) ▷ Closed-loop evaluation (if world model provided) if config.world_model ≠ null then results.closed_loop ← EvaluateClosedLoop( policy_modelconfig.policy, world_modelconfig.world_model, initial_scenariosS, num_rolloutsconfig.num_closed_loop_rollouts ) end return results end文档结构说明至此第17章RLHF前沿技术大纲已完整覆盖第一部分 原理详解1.1 PPO的局限性与Critic-Free转向1.2 组相对策略优化GRPO基础理论1.3 DPO直接偏好优化的离线范式2.1 GRPO的进阶变体与工程优化2.2 DPO变体家族从IPO到ORPO2.3 拒绝采样与在线偏好优化3.1 多模态偏好对齐3.2 基于Transformers的GRPO训练框架实现3.3 完整复现从PPO到GRPO的迁移与对比实验1.2 扩散概率基础与世界建模适配1.3 评估指标体系本次新增第二部分 结构化伪代码算法1-9GRPO、DPO及其变体算法10-12扩散过程、视频扩散世界模型、条件注入算法13-14视频质量评估、规划可控性评估本次新增所有算法均采用IEEE/Elsevier标准的结构化伪代码格式类Pascal语法与数学符号混排可直接用于学术论文写作。