LanePerf: Lane Detection Performance Estimation

• The paper introduces LanePerf as a label-free framework that predicts the F1 score of a frozen lane-detection model using only its outputs.
• It employs a multimodal approach by integrating scene-level cues from a pretrained image encoder with lane features aggregated via a DeepSets-based architecture.
• Empirical results show that LanePerf outperforms confidence-based estimators, achieving lower MAE and higher Spearman correlation under various domain shifts.

Searching arXiv for the specified paper and closely related references. Lane Performance Estimation Framework (LanePerf) is a framework for estimating the performance of a frozen lane-detection model on an unlabeled target domain by predicting the overall $F_1$ score from the model’s own outputs, optionally augmented with image-level embeddings. It was introduced for lane detection in the presence of domain shift, where robustness and safety assessment in new environments is often hindered by the cost of collecting and annotating target-domain data. In this formulation, performance estimation is label-free: the estimator operates without ground-truth lane annotations at test time. LanePerf combines image features and lane features through a pretrained image encoder and a DeepSets-based architecture, with explicit mechanisms for zero-lane detection scenarios and large domain-shift cases (Wu et al., 17 Jul 2025).

1. Motivation and problem setting

Lane detection is a critical component of Advanced Driver-Assistance Systems (ADAS) and Automated Driving System (ADS), because it provides essential spatial information for lateral control. The motivating problem for LanePerf is that domain shifts often undermine model reliability when a detector trained on one domain is deployed in another. The standard remedy—collecting and annotating target-domain data for robustness and safety evaluation—is resource-intensive. The central premise of LanePerf is that estimating model performance without ground-truth labels offers a promising alternative for efficient robustness assessment, especially because performance estimation had remained underexplored in lane detection even though related work existed for image classification (Wu et al., 17 Jul 2025).

The framework is defined around a frozen lane-detection model $f$ trained on a source domain

$D^s=\{(x_i,y_i)\}_{i=1}^M,$

where $x_i$ is an image and

$y_i=\{l_{i1},\ldots,l_{iK_i}\}$

is the set of ground-truth lanes. At test time, one observes only an unlabeled target domain

$D^t=\{x_i\}_{i=1}^N.$

The goal is to learn a performance estimator $E$ that predicts the overall $F_1$ score of $f$ on $D^t$ using only the model outputs:

$f$0

For each image $f$1, the estimator receives the predicted lane instances

$f$2

For each predicted lane $f$3, LanePerf extracts a feature vector $f$4 and a confidence $f$5, and it may also use an image-level embedding $f$6. The output is a scalar $f$7 for each mini-dataset, called a segment, or for the entire target domain. This framing shifts the task from lane detection itself to post hoc estimation of lane-detection quality under distribution shift (Wu et al., 17 Jul 2025).

2. Formalization of label-free performance estimation

The performance-estimation target is not a per-lane label or a per-pixel segmentation score, but the overall $f$8 score of the lane detector on a mini-dataset or target domain. This is important because the estimator must aggregate information across a variable number of predicted lanes and across images, while remaining invariant to the order of lane instances. The formal input structure therefore has two levels: image-level context through $f$9 and set-valued lane-level detections through $D^s=\{(x_i,y_i)\}_{i=1}^M,$0.

LanePerf separates the frozen detector $D^s=\{(x_i,y_i)\}_{i=1}^M,$1 from the learned estimator $D^s=\{(x_i,y_i)\}_{i=1}^M,$2. The detector produces lane candidates, confidences, logits, and intermediate lane features. The estimator then maps those outputs to an estimated score. A plausible implication is that this decoupling makes LanePerf a model-assessment layer rather than a replacement for the detector itself.

The framework is explicitly designed for mini-datasets of roughly fixed size during calibration and evaluation. In the reported setup, the source-domain validation data are partitioned into mini-datasets of approximately 200 frames each, and the estimator is trained to regress from model outputs on such segments to the true segment-level $D^s=\{(x_i,y_i)\}_{i=1}^M,$3 score. This segment-based formulation is also the basis for the rank-correlation analysis reported in the experiments (Wu et al., 17 Jul 2025).

3. Adapted baselines and their limitations

Before introducing the proposed architecture, the work adapts five well-performing performance estimation methods from image classification to lane detection. These establish a baseline but also expose why direct transfer from classification is inadequate for lane-detection outputs (Wu et al., 17 Jul 2025).

• Average Confidence (AC): AC computes the mean softmax score over all detected lanes in the target domain,

$D^s=\{(x_i,y_i)\}_{i=1}^M,$4

Its stated limitations are that softmax scores are overconfident and that the method ignores image context and zero-lane cases.

• Difference of Confidence (DOC): DOC calibrates AC by adding an offset learned on a source validation set $D^s=\{(x_i,y_i)\}_{i=1}^M,$5,

$D^s=\{(x_i,y_i)\}_{i=1}^M,$6

Its limitation is that the offset may overfit to the source domain and not generalize.

• Average Threshold Confidence (ATC): ATC learns a confidence threshold $D^s=\{(x_i,y_i)\}_{i=1}^M,$7 on validation folds so that the fraction of confidences above threshold matches the true $D^s=\{(x_i,y_i)\}_{i=1}^M,$8,

$D^s=\{(x_i,y_i)\}_{i=1}^M,$9

Its limitation is the assumption of a single global threshold, together with the omission of spatial cues and zero-output cases.

• Fréchet Inception Distance (FID): In this adaptation, $x_i$0 is treated as a lane-feature embedding, and the source and target embedding distributions are compared through the Fréchet distance

$x_i$1

A small linear regressor then maps the distance to $x_i$2. Its stated limitation is the Gaussian assumption on embedding distributions and the absence of image content.

• Energy-Based Model (EBM): For each candidate lane $x_i$3, the energy is

$x_i$4

where $x_i$5 are the two binary-classification logits and $x_i$6 is a fitted temperature. The method averages $x_i$7 over all lanes and regresses to $x_i$8. Its limitation is that the energy still depends solely on logits and remains sensitive to out-of-distribution input.

Taken together, these baselines rely either on confidence calibration or on lane-feature distribution matching. The reported limitations motivate LanePerf’s multimodal design: the proposed framework does not use only softmax scores, and it does not use only lane features.

4. Architecture of LanePerf

LanePerf integrates image and lane features into a single regression pipeline. Its image encoder is a pretrained CLIP ViT-B/32, or any foundation model, which extracts a $x_i$9-dimensional feature $y_i=\{l_{i1},\ldots,l_{iK_i}\}$0 intended to capture global scene attributes such as lighting, weather, and road layout. This image pathway introduces scene-level cues that are absent from confidence-only estimators (Wu et al., 17 Jul 2025).

Lane-feature extraction is performed from the detection head of the frozen lane detector $y_i=\{l_{i1},\ldots,l_{iK_i}\}$1, exemplified by CLRNet. For each detected lane $y_i=\{l_{i1},\ldots,l_{iK_i}\}$2, the framework takes the pre-head lane embedding vector $y_i=\{l_{i1},\ldots,l_{iK_i}\}$3, the regressed polynomial parameters if available, and the softmax confidence $y_i=\{l_{i1},\ldots,l_{iK_i}\}$4 together with logits $y_i=\{l_{i1},\ldots,l_{iK_i}\}$5. These are concatenated into a fixed-length vector $y_i=\{l_{i1},\ldots,l_{iK_i}\}$6.

To aggregate a variable number of lanes, including the case of no lanes at all, LanePerf uses DeepSets (Zaheer et al., 2017). With the lane set

$y_i=\{l_{i1},\ldots,l_{iK_i}\}$7

the aggregation is

$y_i=\{l_{i1},\ldots,l_{iK_i}\}$8

and, when $y_i=\{l_{i1},\ldots,l_{iK_i}\}$9, the sum uses a learned default embedding $D^t=\{x_i\}_{i=1}^N.$0. The lane representation is then

$D^t=\{x_i\}_{i=1}^N.$1

where $D^t=\{x_i\}_{i=1}^N.$2 is a two-layer FCN with ReLU and $D^t=\{x_i\}_{i=1}^N.$3 is another two-layer FCN. In compact form, the DeepSets function is

$D^t=\{x_i\}_{i=1}^N.$4

The final score-regression stage concatenates the image and lane representations,

$D^t=\{x_i\}_{i=1}^N.$5

and feeds them to a two-layer FCN followed by a sigmoid to produce $D^t=\{x_i\}_{i=1}^N.$6. The zero-lane mechanism is explicit: a learnable default lane embedding $D^t=\{x_i\}_{i=1}^N.$7 ensures that the network still receives a non-zero input when no lanes are predicted. The paper further states that the pretrained $D^t=\{x_i\}_{i=1}^N.$8 remains robust under domain shift and injects scene-level cues that complement lane confidences (Wu et al., 17 Jul 2025).

The architectural rationale is stated directly in the discussion: multimodal fusion of scene-level cues and per-lane confidences compensates for missing or overconfident softmax signals; DeepSets naturally handles a variable, even empty, set of lanes; and a pretrained image encoder provides robustness under heavy domain shift.

5. Training protocol and empirical results

LanePerf is trained by minimizing mean squared error over validation segments:

$D^t=\{x_i\}_{i=1}^N.$9

The optimizer is Adam with learning rate $E$0, weight decay $E$1, and batch size 16 segments. Standard $E$2 regularization is applied on weights, and dropout is set to 0.1 in FCN layers. The lane detector $E$3 is trained on one source subset per shift type: Residential Area for scene shift, Clear for weather shift, and Daytime for hours shift. The performance estimator is calibrated on source-domain validation splits. Evaluation is conducted on 4–5 target domains for each dimension: Scene $E$4, Weather $E$5, and Hours $E$6 (Wu et al., 17 Jul 2025).

Two metrics are used. Mean Absolute Error is

$E$7

and Spearman’s rank correlation is

$E$8

where $E$9 is the rank difference between the true and estimated $F_1$0 of the $F_1$1-th segment.

Method	MAE	Spearman's $F_1$2
LanePerf	0.117	0.727
AC	0.130	0.669
DOC	0.176	0.669
ATC	0.183	0.635
FID	0.168	0.628
EBM	0.213	0.559

These values are the overall averages across all 10 cross-domain transfers on OpenLane. Per-shift averages further report Scene: LanePerf $F_1$3 versus AC $F_1$4; Weather: LanePerf $F_1$5 versus AC $F_1$6; and Hours: LanePerf $F_1$7 versus AC $F_1$8. In the ablations, image only yields high MAE and low $F_1$9, lane only is moderate, and image plus lane, the full LanePerf, is best and most stable. In encoder ablations, CLIP, ViT, and DINOv2 all outperform AC, while CLIP is best for weather shifts. In zero-lane sequences, AC defaults to zero and incurs large errors, whereas LanePerf uses $f$0 to predict a nonzero $f$1, greatly reducing error (Wu et al., 17 Jul 2025).

6. Interpretation, practical implications, and scope

LanePerf is presented as a lightweight, general-purpose framework for accurately estimating lane-detection performance without ground-truth labels, including challenging out-of-distribution settings. Its practical implication for ADAS and ADS testing is large-scale, label-free validation of lane-detection modules on fleet data. The reported use case is operational rather than merely diagnostic: the framework can quickly surface corner-case scenarios, including unusual scene layouts, night glare, and heavy rain, by monitoring predicted versus expected performance. The stated consequence is reduced annotation burden and accelerated safety certification cycles (Wu et al., 17 Jul 2025).

A common misconception is to treat performance estimation as equivalent to uncertainty estimation from detector confidences alone. The baseline comparisons in LanePerf argue against that simplification. Softmax-derived summaries such as AC, DOC, and ATC are explicitly reported to ignore image context, to be overconfident, or to fail in zero-lane settings. LanePerf’s design suggests that the relevant signal for post hoc quality estimation is not confined to detector logits; it also resides in global scene attributes and in the structure of the detected lane set.

The framework’s scope should also be stated narrowly. Its target is the estimation of a detector’s segment-level or domain-level $f$2 score from unlabeled inputs and detector outputs. It is not a lane detector, not a retraining procedure for the frozen detector, and not a substitute for the underlying evaluation metric. Rather, it is a learned estimator of that metric under missing labels.

7. Terminological ambiguity with other uses of “LanePerf”

The acronym “LanePerf” is also used in a distinct 2024 paper on pavement engineering, where it denotes a multi-task recurrent neural network for lane-level pavement performance prediction from segment-level data rather than a framework for estimating the performance of a lane detector (Wang et al., 2024). In that work, the input consists of historical segment-level pavement measurements such as PCI, PQI, and RQI, together with static and time-varying covariates; the architecture uses a shared two-layer LSTM, task-specific LSTM branches, concatenation with auxiliary features, and per-lane output layers; and the output is a lane-level deterioration forecast evaluated by Mean Absolute Percentage Error.

The two meanings are therefore methodologically and substantively separate. The 2025 LanePerf estimates a lane detector’s unlabeled target-domain $f$3 score under scene, weather, and hours shifts (Wu et al., 17 Jul 2025). The 2024 LanePerf predicts lane-level pavement deterioration trajectories from historical road-condition data (Wang et al., 2024). The shared acronym can create bibliographic confusion, but the underlying tasks, inputs, outputs, and evaluation criteria are different.