Title: TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
Venue: NeurIPS 2025
Accepted: yes
Keywords: Autonomous Driving
Abstract: Topology reasoning, which unifies perception and structured reasoning, plays a vital role in understanding intersections for autonomous driving. However, its performance heavily relies on the accuracy of lane detection, particularly at connected lane endpoints. Existing methods often suffer from lane endpoints deviation, leading to incorrect topology construction. To address this issue, we propose TopoPoint, a novel framework that explicitly detects lane endpoints and jointly reasons over endpoints and lanes for robust topology reasoning. During training, we independently initialize point and lane query, and proposed Point-Lane Merge Self-Attention to enhance global context sharing through incorporating geometric distances between points and lanes as an attention mask . We further design Point-Lane Graph Convolutional Network to enable mutual feature aggregation between point and lane query. During inference, we introduce Point-Lane Geometry Matching algorithm that computes distances between detected points and lanes to refine lane endpoints, effectively mitigating endpoint deviation. Extensive experiments on the OpenLane-V2 benchmark demonstrate that TopoPoint achieves state-of-the-art performance in topology reasoning (48.8 on OLS). Additionally, we propose DET$_p$ to evaluate endpoint detection, under which our method significantly outperforms existing approaches (52.6 v.s. 45.2 on DET$_p$). The code is released at https://github.com/Franpin/TopoPoint.

TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving

Abstract

Topology reasoning, which unifies perception and structured reasoning, plays a vital role in understanding intersections for autonomous driving. However, its performance heavily relies on the accuracy of lane detection, particularly at connected lane endpoints. Existing methods often suffer from lane endpoints deviation, leading to incorrect topology construction. To address this issue, we propose TopoPoint, a novel framework that explicitly detects lane endpoints and jointly reasons over endpoints and lanes for robust topology reasoning. During training, we independently initialize point and lane query, and proposed Point-Lane Merge Self-Attention to enhance global context sharing through incorporating geometric distances between points and lanes as an attention mask . We further design Point-Lane Graph Convolutional Network to enable mutual feature aggregation between point and lane query. During inference, we introduce Point-Lane Geometry Matching algorithm that computes distances between detected points and lanes to refine lane endpoints, effectively mitigating endpoint deviation. Extensive experiments on the OpenLane-V2 benchmark demonstrate that TopoPoint achieves state-of-the-art performance in topology reasoning (48.8 on OLS). Additionally, we propose DET p to evaluate endpoint detection, under which our method significantly outperforms existing approaches (52.6 v.s. 45.2 on DET p ). The code is released at https://github.com/Franpin/TopoPoint .

1 Introduction

In autonomous driving scenarios, perceiving lane markings and traffic elements on the road surface is critical for understanding complex intersection environments. To enable accurate interpretation of the scene and determine feasible driving directions, it is essential to infer both lane-lane topology and lanetraffic element topology. With the growing trend of end-to-end autonomous driving systems[1, 2, 3], perception and reasoning have become increasingly integrated into a unified task, referred to as topology reasoning[4, 5, 6, 7, 8]. This task also plays a vital role in high-definition (HD) map learning[9, 10, 11, 12] and supports downstream modules such as planning and control.

As a continuation of the lane detection task, topology reasoning task need to uniformly process lanes, traffic elements, and their corresponding topological relationships, so the query-based architecture has become the mainstream solution. In this pipeline, the multiple lanes are encoded and predicted through multiple independent queries, as shown in Figure 1(a). However, since the lane endpoints are actually attached to lane query and are affected by the supervised learning of multiple lanes, it is difficult to ensure that the multiple endpoints of the final prediction can strictly coincide, which is called the endpoint deviation problem. This problem already explored preliminarily as early as in the era of lane detection, e.g., the method STSU[13] aligns the endpoints by moving the entire lane, while the method LaneGAP[14] adopts a path-wise modeling approach, predicting complete

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lane paths by merging connected lane pieces. However, due to the suboptimal performance of lane detection, these methods have been replaced. A recent work, TopoLogic[15], has once again noticed this problem. It integrates the lane-lane geometric distance and semantic similarity to alleviate the interference of the endpoint deviation in topology reasoning, instead of rectifying the issue itself. Therefore, lane detection is still inaccurate, which means that the endpoint deviation problem has not been completely resolved.

To address the aforementioned issues, we propose TopoPoint, a novel framework that introduces explicit endpoint detection and fuses features from both lanes and endpoints to enhance topology reasoning, as is illustrated in Figure 1(b). By reasoning over the topological relationship between endpoints and lanes, TopoPoint effectively mitigates the endpoint deviation problem. To enable point detection and facilitate feature interaction between points and lanes during training, we design the point-lane detector, independently initializing point query and lane query. These queries are supervised at the output by separate objectives for lane detection and endpoint detection. We further propose Point-Lane Merge Self-Attention (PLMSA), and it concatenates point and lane query and leverages geometric distances as attention masks to enhance global context sharing. To enhance point-lane feature interactions, we introduce the Point-Lane Graph Convolutional Network (PLGCN), and it models the topological relationships between points and lanes by constructing an adjacency matrix. This enables bidirectional message passing between point and lane features through Graph Convolutional Network (GCN)[16]. PLGCN serves as a key component of our Unified Scene Graph Network. This joint learning process significantly enhances the representation capability of both endpoints, lanes and traffic elements, thereby improving topology reasoning performance. During inference, we propose the Point-Lane Geometry Matching (PLGM) algorithm, and it computes geometric distances between detected endpoints and the start and end points of lanes. This allows us to refine lane endpoints by matching points to lanes based on their geometric proximity, effectively mitigating the endpoint deviation issue. Our contributions are summarized as follows:

We identify that the endpoint eviation issue in current methods stems from the fact that lane endpoints are simultaneously supervised by multiple lanes. To tackle this, we propose independently detecting endpoints and Point-Lane Geometry Matching algorithm to refine lane endpoints.

We introduce TopoPoint, a novel framework designed to enhance topology reasoning by incorporating explicit endpoint detection. Within TopoPoint, point query and lane query exchange global contextual information through the proposed Point-Lane Merge Self-Attention, and their feature interaction is further reinforced by the Point-Lane Graph Convolutional Network.

All experiments are conducted on the OpenLane-V2[17] benchmark, where our method outperforms existing approaches and achieves state-of-the-art performance. In addition, We introduce DET p for evaluating endpoint detection, and our method achieves notable improvements.

2 Related Work

2.1 Lane Detection

Lane detection is essential for autonomous driving, providing structural cues for road perception[9, 12, 11, 10] and motion planning[3]. Traditional methods typically use semantic segmentation to identify lane areas in front-view images, but they often struggle with long-range consistency and occlusions.

To overcome these limitations, vector-based approaches model lanes as sparse representations. Recent advances in 3D lane detection have been driven by sparse BEV-based object detectors like DETR3D[18] and PETR[19], which use sparse query and multi-view geometry to reason directly in 3D space. These ideas have inspired a new wave of lane detectors. For instance, CurveFormer[20] represents lanes with 3D line anchors and introduces curve query that encode strong positional priors. Anchor3DLane[21] extends LaneATT[22]'s line anchor pooling and incorporates both intrinsic and extrinsic camera parameters to accurately project 3D anchor points onto front-view feature maps. PersFormer[23] leverages deformable attention to learn the transformation from front-view to BEV space, improving spatial alignment. LATR[24] further refines lane modeling by decomposing it into dynamic point-level and lane-level query, enabling finer topological representation.

2.2 Topology Reasoning

Topology reasoning in autonomous driving aims to interpret road scenes and define drivable routes. STSU[13] encodes lane query for topology prediction by DETR[25]. LaneGAP[14] applies shortest path algorithms to transform lane-lane topology into overlapping paths. TopoNet[26] combines Deformable DETR[27] with GNN[28] to aggregate features from connected lanes. TopoMLP[29, 30] leverages PETR[19] for lane detection and uses a multi-layer perceptron for topology reasoning. TopoLogic[15] integrates geometric and semantic information by combining lane-lane geometric distance with semantic similarity. TopoFormer[31] introduces unified traffic scene graph to explicitly model lanes. SMERF[32] improves lane detection by incorporating SDMap as an additional input, while LaneSegNet[33] uses Lane Attention to identify lane segments. In our work, We introduce endpoint detection to enhance topology reasoning and mitigate endpoint deviation.

3 Method

3.1 Problem Definition

Given surround-view images captured by multiple cameras mounted on a vehicle, the topology reasoning task includes: 3D lane centerline detection[34, 19, 35, 36, 23] in the bird's-eye view (BEV) space, 2D traffic element detection[37] in the front-view image, topology reasoning[26, 33, 17, 32] among lane centerlines and topology reasoning between lane centerlines and traffic elements. All lane centerlines are represented by multiple sets of ordered point sequences L = { l i ∈ R k × 3 | i = 1 , 2 , . . . , n l } , where n l is the number of lane centerlines and k is the number of points on the lane centerline. All traffic elements are represented using multiple 2D bounding boxes T = { t i ∈ R 4 | i = 1 , 2 , . . . , n t } , where n t is the number of traffic elements. The lane-lane topology, which encodes the connectivity between lanes, is represented by an adjacency matrix G ll . The lane-traffic element topology, capturing the association between lanes and traffic elements, is represented by another adjacency matrix G lt . In addition, the framework includes point detection and point-lane topology reasoning. A set of candidate points P = { p i ∈ R 3 | i = 0 , 1 , 2 , . . . n p } is constructed by de-duplicating all endpoints of lane centerlines, where n p is the number of unique endpoints. The point-lane topology G pl is created by checking whether the point lies on lane centerline.

3.2 Overview

As illustrated in Figure 2, our proposed TopoPoint framework consists of traffic detector, point-lane detector, geometric attention bias, topology head and point-lane result fusion. We downsample the multi-view by a factor of 0.5, while keeping the front-view at its original resolution. During training, all images are passed through ResNet-50[38] pretrained on ImageNet[39] with FPN[40] to extract multi-scale features. These features are then encoded into BEV representations using BevFormer[41] encoder. In the traffic detector, front-view features are directly processed by Deformable DETR[27] to produce traffic query ˆ Q t . In the point-lane detector, point query Q p and lane query Q l interact via Point-Lane Merge Self-Attention, which computes geometric attention bias serving as an attention mask to enhance global information sharing. The resulting queries then perform cross-attention with BEV features. Then Q p and Q l together with ˆ Q t , are fed into Unified Scene Graph Network. The topology head computes point-lane topology, lane-lane topology and lane-traffic topology. During inference, predicted points and lanes are fused via Point-Lane Geometry Matching algorithm to refine lane endpoints and effectively mitigate the endpoint deviation problem.

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3.3 Traffic Detector

To detect traffic elements in the front-view image, we initialize traffic element query Q t , which interact with multi-scale front-view features F fv via Deformable DETR to compute cross-attention and produce updated representations ˆ Q t . The ˆ Q t are then passed through the Traffic Head to predict 2D bounding boxes ˆ T . The process is as follows:

where Q t ∈ R N t × d , F fv ∈ R H F × W F × d and ˆ T ∈ R N t × 4 , N t denotes the number of Q t , d denotes the feature dimension, ( H fv , W fv ) denotes the size of F bev .

3.4 Point-Lane Detector

We independently initialize point query Q p and lane query Q l . These queries first interact through Point-Lane Merge Self-Attention to exchange global information. The updated queries then compute cross-attention with the BEV features, followed by two separate feed-forward networks (FFNs). The resulting Q p and Q l are subsequently fed into Unified Scene Graph Network, where they aggregate features from each other via graph convolution networks (GCNs). The enhanced representations are finally used by the point head and lane head to regress endpoints and lane centerlines, respectively.

Point-Lane Merge Self-Attention. We first concatenate Q p and Q l along the instance dimension to form Q pl . Q pl is then used as the query, key, and value in the self-attention computation. The definition of Q pl as follows:

where Q p ∈ R N p × d , Q l ∈ R N l × d , Q pl ∈ R N pl × d , N p denotes the number of Q p , N l denotes the number of Q l , N pl = N p + N l and d denotes the feature dimension. To incorporate the geometric relationships between points and lanes in the BEV space, we compute their pairwise

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geometric distances based on the predicted points ˆ P l -1 = { ˆ p i ∈ R 3 | i = 1 , 2 , . . . , N p } and lanes ˆ L l -1 = { ˆ l i ∈ R k × 3 | i = 1 , 2 , . . . , N l } from the previous decoder layer, where k denote the number of points in each lane. These distances are then transformed by a learnable mapping function f map to obtain geometric bias matrix M pp , M pl and M ll , as follows:

where ˆ l s i ∈ R 3 denotes the start point of ˆ l i , ˆ l e i ∈ R 3 denotes the end point of ˆ l i , D ll ∈ R N l × N l denote the L1 distance from the start points to the end points in ˆ L l -1 , and D pl ∈ R N p × N l denote the minimum L1 distance from ˆ P l -1 to the endpoints of ˆ L l -1 . Notably, f map = e -x p λ · ˆ σ is proposed in TopoLogic[15], α, λ are learnable parameters, and ˆ σ is the standard deviation of distance matrix D .

To compute self-attention, we concatenate M pl , M ll to form geometric attention bias, which is added to the attention weights computed from Q pl . The self attention process is described as follows:

where Z ∈ R N p × N p denotes the zero matrix, M pl ∈ R N p × N l , M ll ∈ R N l × N l and LN demotes the layer normalization.

Point-Lane Deformable Cross Attention . After self-attention, Q p and Q l are used to compute deformable cross-attention with the BEV feature. Specifically, we independently initialize two sets of learnable reference points, R p and R l , corresponding to Q p and Q l , which attends to the BEV feature via deformable cross-attention using its own reference points. The results are then passed through two separate feed-forward networks (FFNs). The process is described as follows:

where R p ∈ R N p × 3 , R l ∈ R N l × 3 , F bev ∈ R H B × W B × d denotes BEV feature map, ( H B , W B ) denotes the BEV size of F bev .

Unified Scene Graph Network. We construct a Unified Scene Graph Network by assembling the Q p , Q l , and Q t , as illustrated in Figure 3(a). To enhance the interaction between point and lane representations, we further introduce the Point-Lane Graph Convolutional Network (PLGCN), as shown in Figure 3(b). The PLGCN is designed to facilitate bidirectional feature aggregation between Q p and Q l based on their geometric relationships. The structure of the PLGCN is as follows:

In the Unified Scene Graph Network, Q p and Q l first interact with each other through the first Point-Lane Graph Convolutional Network (PLGCN 1 ) to generate updated features Q 1 p and Q 1 l . Then Q 1 l is processed through two separate GCNs: GCN ll aggregates information from Q 1 l itself to enhance intra-lane relationships, while GCN lt aggregates information from ˆ Q t to incorporate semantic context. The outputs from these two branches are concatenated and downsampled to form Q 2 l . Finally, a second round of Point-Lane Graph Convolutional Network (PLGCN 2 ) is applied to Q 2 l and Q 1 p , yielding the final enhanced features Q 3 l and Q 3 p , which are used as the output of the Point-Lane detector decoder layer. The overall process can be formulated as:

where λ 1 , λ 2 denotes the learnable parameters. GCN ( X,A ) = σ ( ˆ AXW ) , X denotes the input, W denotes the learnable weight matrix, A denotes the adjacency matrix, ˆ A denotes the normalized A and σ denotes sigmoid[42] function. M ll = I + M ll + M ⊤ ll , I ∈ R N l × N l denotes the identity matrix, M pl , M ll is derived within the Point-Lane Merge Self-Attention, G pl , G lt is derived within the Topology Head from the previous decoder layer. Downsample denotes the Linear-layer.

Point-Lane Head. After passing through the Unified Scene Graph Network, we obtain the enhanced point query ˆ Q p and lane query ˆ Q l , which are fed into the PointHead and LaneHead, respectively, to produce the predicted point set ˆ P = { ˆ P reg , ˆ P cls } and lane set ˆ L = { ˆ L reg , ˆ L cls } , as follows:

where ˆ P reg ∈ R N p × 3 and ˆ L reg ∈ R N p × k × 3 denote the regressed points and lanes, respectively, ˆ P cls ∈ R N p × 1 and ˆ L cls ∈ R N l × 1 denotes classification scores for points and lanes, LaneHead and PointHead each consist of two separate MLP branches for regression and classification.

3.5 Topology Head

To predict the point-lane topology, lane-lane topology and lane-traffic topology. We perform topology reasoning based on the enhanced features ˆ Q p , ˆ Q l and ˆ Q t obtained from the detectors. We encode these features using separate MLPs and compute their pairwise similarities as the topology reasoning outputs. The process is formulated as follows:

where ˆ G pl ∈ R N p × N l denotes the point-lane topology, ˆ G ll ∈ R N l × N l denotes the lane-lane topology, ˆ G lt ∈ R N l × N t denotes the lane-traffic topology.

3.6 Training

During the training phase, the overall loss of TopoPoint is composed of detection loss and topology reasoning loss. The detection loss includes the traffic element detection loss, point detection loss and lane detection loss. The topology reasoning loss consists of the point-lane topology loss, lane-lane topology loss and lane-traffic topology loss. The total loss is defined as:

where L t , L p and L l denote the traffic element detection loss, point detection loss and lane detection loss, respectively. L pl , L ll and L lt represent the losses for point-lane topology, lane-lane topology and lane-traffic topology reasoning. λ t , λ p , λ l , λ pl , λ ll and λ lt are the corresponding loss weights. Specially, the L p and L l consist of classification loss and regression loss, where the classification loss employs the Focal loss[43] and the regression loss utilizes the L1 loss[44]. For L t , in addition to classification loss and regression loss, we incorporate the GIoU loss[45] to further improve localization accuracy. For topology reasoning, we adopt the focal loss for both L pl , L ll and L lt .

3.7 Inference

To mitigate the endpoint deviation issue in lane prediction during inference, we propose the PointLane Geometry Matching (PLGM) algorithm. This method first filters out high-confidence predictions from ˆ P reg and ˆ L reg using their associated classification scores ˆ P cls and ˆ L cls . For each selected point ˆ P i ∈ ˆ P select , we identify a set of nearby lane endpoints N i from ˆ L select based on their geometric distances in the BEV space. If the matching is found, the selected point and its neighboring lane endpoints are jointly averaged to compute refined endpoint ˆ E i , which is then used to update the corresponding lane predictions. This refinement leads to better-aligned lane endpoints and improved overall topology consistency. The complete procedure is illustrated in Algorithm 1.

Algorithm 1: Point-Lane Geometry Matching Algorithm

Input: Predicted points ˆ P reg , ˆ P cls ; predicted lanes ˆ L reg , ˆ L cls ; classification thresholds τ p , τ l ; geometry distance threshold δ .

Output: Refined lanes ˆ L ref

Step 1: High-Confidence Filtering

Filter points with high classification scores: ˆ P select = { ˆ P i | ˆ P i > τ p

Filter lanes with high classification scores: L select = { L j reg | L cls > τ l }

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4 Experiment

4.1 Dataset and Metric

Dataset. We evaluate TopoPoint on the large-scale topology reasoning benchmark OpenLaneV2[17], which is constructed based on Argoverse2[46] and nuScenes[47]. The dataset provides comprehensive annotations for lane centerline detection, traffic element detection, and topology reasoning tasks. OpenLane-V2 is divided into two subsets: subset_A and subset_B , each containing 1,000 scenes captured at 2 Hz with multi-view images and corresponding annotations. Both subsets include annotations for lane centerlines, traffic elements, lane-lane topology, and lane-traffic topology. Notably, subset_A provides seven camera views as input, while subset_B includes six views.

Metric. We adopt the evaluation metrics defined by OpenLane-V2, including DET l , DET t , TOP ll , and TOP lt , all of which are computed based on mean Average Precision (mAP). Specifically, DET l quantifies similarity by averaging the Fréchet distance under matching thresholds of 1.0, 2.0, and 3.0. DET t evaluates detection quality for traffic elements using the Intersection over Union (IoU) metric, averaged across different traffic categories. TOP ll and TOP lt measure the similarity of the predicted lane-lane topology matrix and lane-traffic topology matrix, respectively. The overall OpenLane-V2 Score (OLS) is calculated as follows:

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All evaluation metrics are computed based on the latest version (v2.1.0) of OpenLane-V2, which is available on the official OpenLane-V2 GitHub repository. In addition, to evaluate the performance of endpoint detection, we define a custom metric DET p , which is computed as the average over match thresholds T = { 1 . 0 , 2 . 0 , 3 . 0 } based on the point-wise Fréchet distance, as follows:

4.2 Implementation Details

Model details. The multi-view images have a resolution of 2048 × 1550 pixels, with the front view specifically cropped and padded to match 2048 × 1550 . Notably, all multi-view inputs are downsampled by a factor of 0.5 before being fed into the backbone, except for the front view, which is directly processed at the original resolution. A pretrained ResNet-50 is adopted as the backbone, and a Feature Pyramid Network is used as the neck to extract multi-scale features. The hidden feature dimension d is set to 256. BEV grid size is configured to 200 × 100 . The number of traffic element query N t , point query N p and lane query N l are set to 100, 200 and 300, respectively. The sampled points number k of each lane is set to 11. The decoder consists of 6 layers. Following TopoLogic, the learnable parameters λ and α in the mapping function f map are initialized to 0.2 and 2.0, respectively, λ 1 and λ 2 in A pl are both initialized to 1.0. The detection loss weights λ t , λ p , λ l and are all set to 1.0, while the topology reasoning loss weights λ ll and λ lt are both set to 5.0. In inference, the classification thresholds for filtering high-confidence predictions are both set to τ p = τ l = 0 . 3 . For geometric matching, the distance threshold δ is set to 1.5 meters to determine valid point-lane associations.

Training details. We train the traffic detector, point-lane detector and topology head in an end-to-end manner. TopoPoint is trained using the AdamW optimizer with a cosine annealing learning rate schedule, starting at 2 . 0 × 10 -4 with a weight decay of 0.01. All experiments are conducted for 24 epochs on 8 Tesla V100 GPUs with a batch size of 8.

4.3 Comparison on OpenLane-V2 Dataset

We compare TopoPoint with existing methods on the OpenLane-V2 benchmark, and the results are summarized in Table 1. On subset_A , TopoPoint achieves 48.8 on OLS, surpassing all previous approaches and achieving state-of-the-art performance. Notably, despite TopoFormer leveraging a pretrained lane detector, our method achieves superior performance ( 48.8 v.s. 46.3 on OLS). Built upon TopoLogic, TopoPoint demonstrates superior performance in lane detection ( 31.4 v.s. 29.9 on DET l ) and shows a substantial improvement in traffic element detection ( 55.3 v.s. 47.2 on DET t ). Furthermore, it outperforms in lane-lane topology reasoning ( 28.7 v.s. 23.9 on TOP ll ) and achieves better results in lane-traffic topology reasoning ( 30.0 v.s. 25.4 on TOP lt ). Additionally, there is a notable improvement in the endpoint detection ( 52.6 v.s. 45.2 on DET p ). Meanwhile, TopoPoint also achieves state-of-the-art performance on subset_B ( 49.2 on OLS, 45.1 on DET p ), further demonstrating its effectiveness.

4.4 Ablation Study

We conduct ablation studies on several key components of TopoPoint using OpenLane-V2 subset_A .

Impact of each module. We conduct an ablation study to assess the impact of each module on topology reasoning performance. As shown in the Table 2, keeping the original front-view scale (scale =1.0) improves traffic element detection ( 53.8 v.s. 46.8 on DET t ), enhancing lane-traffic topology reasoning ( 27.0 v.s. 24.3 on TOP lt ). Adding Point-Lane Merge Self-Attention (PLMSA) boosts lane and endpoint detection ( 30.2 v.s. 29.4 on DET l , 49.8 v.s. 44.8 on DET p ), leading to better lane-lane and lane-traffic topology reasoning ( 27.2 v.s. 23.8 on TOP ll , 28.5 v.s. 27.0 on TOP lt ). Incorporating Point-Lane Graph Convolutional Network (PLGCN) further improves detection ( 30.8 v.s. 30.2 on DET l , 51.8 v.s. 49.8 on DET p ). Finally, the Point-Lane Geometry Matching (PLGM) algorithm refines lane endpoints during inference, mitigating endpoint deviation and enhancing lane and point detection ( 31.4 v.s. 30.8 on DET l , 52.6 v.s. 51.8 on DET p ).

Effect of different GCNs. We investigate the impact of various GCN designs on topology reasoning performance. As shown in Table 3, adding the lane-lane GCN and lane-traffic GCN improves lane

detection ( 30.6 v.s. 29.8 v.s. 28.9 on DET l ), thereby enhancing both lane-lane and lane-traffic topology reasoning ( 27.4 v.s. 26.9 v.s. 25.6 on TOP ll , 28.8 v.s. 27.1 v.s. 26.4 on TOP lt ). Moreover, introducing two variants of the point-lane GCN effectively boosts both lane and endpoint detection performance ( 31.4 v.s. 30.9 v.s. 30.6 on DET l , 52.6 v.s. 51.9 v.s. 50.5 on DET p ).

Image scales set up. We investigate the impact of different image scaling strategies on topology reasoning performance. As shown in the Table 4, keeping the front-view image at its original resolution improves the performance of traffic element detection ( 55.3 v.s. 48.6, 54.7 v.s. 48.3 on DET t ). On the other hand, downscaling the multi-view images by a factor of 0.5 slightly boosts lane detection performance ( 31.2 v.s. 30.5, 31.4 v.s. 30.8 on DET l ).

Effect of point and lane query numbers. We investigate the impact of varying the number of point and lane query on topology reasoning performance. As shown in the Table 5, increasing the number of point query from 100 to 200 improves endpoint detection ( 51.8 v.s. 49.7 on DET p ), which in turn enhances lane detection performance ( 30.7 v.s. 29.5 on DET l ). However, further increasing the number from 200 to 300 introduces more negative point samples, leading to degraded endpoint detection (51.4 v.s. 52.6 on DET p ) and consequently worse lane detection performance (30.8 v.s. 31.4 on DET l ). On the other hand, increasing the number of lane query from 200 to 300 consistently improves lane detection accuracy( 31.4 v.s. 30.7 on DET l ).

4.5 Qualitative Results

Figure 4 provides a qualitative result comparison between TopoLogic and our TopoPoint. On the whole, both TopoLogic and TopoPoint yield good results. Nevertheless, as TopoLogic lacks a direct enhancement to lane detection itself, it is more likely to produce incorrect or missing lanes, thereby resulting in inaccurate or absent topologies. Benefit from the independent endpoint modeling and the

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interaction between points and lanes, TopoPoint has managed to avoid such situations as much as possible. Moreover, it is evident that TopoPoint eradicates the endpoint deviation at lane connections, which still exist in TopoLogic. Both Figure 5 and Figure 6 provide more qualitative results comparison between TopoLogic and our TopoPoint.

5 Conclusion

In this paper, we identify the endpoint deviation issue in existing topology reasoning methods. To tackle this, we propose TopoPoint, which introduces explicit endpoint detection and strengthens point-lane interaction through Point-Lane Merge Self-Attention and Point-Lane GCN. We further design a geometry matching strategy to refine lane endpoints. Experiments on OpenLane-V2 show that TopoPoint achieves state-of-the-art performance in OLS. Additionally, we introduce DET p metric for evaluating endpoint detection, where TopoPoint also achieves significant improvement.

Impact. TopoPoint improves 3D lane detection by addressing endpoint deviation and enhancing topology reasoning, benefiting autonomous driving tasks like planning and mapping.

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Endpoint & Lane Detection

Multi-view Input

Front-view

Lane-Traffic Topology

Lane-Lane Topology

TopoPoint

TopoLogic

GT

TopoPoint

TopoLogic

GT

一

Multi-view Input

Endpoint & Lane Detection

Lane-Lane Topology

Lane-Traffic Topology

Front-view

Multi-view Input

TopoPoint

TopoLogic

GT

TopoPoint

TopoLogic

Lane-Lane Topology

Front-view

Lane-Traffic Topology

GT

Endpoint & Lane Detection

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to solve the endpoint deviation problem in lane-phase inference, which is central to understanding autonomous intersections.  The authors added a separate branch for detecting endpoints and designed Point-Lane Merge Self-Attention and Point-Lane  Graph Convolutional Network to exchange global information between endpoints and lane queries.  During the inference stage, the predicted lane endpoints are aligned with the detected endpoints through geometric matching to correct remaining errors. The method demonstrated its effectiveness by achieving state-of-the-art performance on the OpenLane-V2 benchmark.  The introduction of the $DET_p$ metric further enhances the work's originality by providing a new tool for evaluating a previously less quantified aspect of lane detection performance.\n\nSTRENGTHS AND WEAKNESSES:\n[Strengths]\n1. Novel Solution for endpoint detection in autonomous driving:\nThis paper identifies and emphasizes the importance of the “endpoint deviation” problem, which is practical but often overlooked in the field of autonomous driving topology inference. It specifically explains how minor discrepancies at the endpoints that occur when predicting multiple lanes independently can cause problems for subsequent modules. As a solution, the core idea of explicitly detecting lane endpoints as separate, independent entities and using them to refine lane predictions and enforce topological consistency is highly innovative and directly addresses the identified problem.\nThe core idea of explicitly detecting lane endpoints as separate entities and then using them to refine lane predictions and reason about topology is novel and directly tackles this deviation problem.\n\n2. Novel metric for endpoint detection:\nTo directly demonstrate the improvement in endpoint detection accuracy, we propose a new evaluation metric, $DET_p$. This metric shows a significant performance improvement over existing methods, providing strong quantitative evidence to support the claims made in this paper, from problem definition to verification.\n\n3. Detailed and Reproducible Methodology:\nThe paper provides detailed explanations and mathematical formulas for each component of the proposed TopoPoint framework. From the geometric bias calculation of PLMSA to the complex GCN interactions within USGN, the learning loss function configuration, and hyperparameter settings, it provides sufficient information necessary for research reproduction.\n\n[Weaknesses]\n1. Lack of Real-time and Computational Cost Analysis:\nThere is no analysis of the inference speed (FPS) or computational cost (GFLOPs) of the proposed model. The TopoPoint architecture, especially USGN, which includes multiple GCN modules, is conceptually complex and is expected to require significant computational power. Real-time performance is critical for autonomous driving systems, so it is necessary to analyze whether the performance improvement compared to simpler models justifies the additional computational cost.\n\n2. Proof of Impact on Downstream Tasks:\nThe paper argues that solving the endpoint deviation problem is important for subsequent path planning and control tasks, but it does not provide direct experimental evidence to support this claim. If a quantitative evaluation of how much improved topological accuracy actually improves the performance of the planning module had been included, the practical usefulness of the proposed method and the logical persuasiveness of the paper would have been greatly enhanced.\n\n3.  Lack of calibration sensitivity analysis:\nIn their conclusion, the authors acknowledged that TopoPoint relies on accurate camera calibration, but they did not provide a sensitivity analysis of how performance degrades depending on the degree of calibration error. This is important information for evaluating the robustness of the model to sensor errors that may occur in actual vehicle applications.\n\nQUESTIONS:\n1. Analysis of Inter-Module Error Propagation:\nWhether the performance degradation of ($DET_t$) only affects the lane-traffic topology ($TOP_lt$), or whether it can also negatively affect the geometric accuracy of lane($DET_l$) and endpoint ($DET_p$) predictions through feature propagation.\n\n2. Verification of Generalization Performance on Other Datasets:\nAll experiments in this paper were performed only on the OpenLane-V2 benchmark. To prove the generalizability of the TopoPoint methodology, we would like to know if there are additional experimental results on other tasks in nuScenes or Argoverse2, or on different datasets (e.g., Waymo Open Dataset).\n\n3. Sensitivity on calibration accuracy:\nTopoPoint explicitly acknowledges that it relies on accurate calibration, could you provide sensitivity analysis experimental results showing the changes in each evaluation indicator for each correction error range.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces TopoPoint, a framework aimed at improving topology reasoning in autonomous driving by explicitly detecting lane endpoints and jointly modeling lanes and endpoints. The authors identify the persistent issue of endpoint deviation in existing lane topology models and address it by proposing: (1) Point-Lane Merge Self-Attention for to exchange global information between endpoints and lanes, (2) Point-Lane Graph Convolutional Network for message passing between lanes and endpoints, and (3) Point-Lane Geometry Matching for refinement during inference. Evaluations on the OpenLane-V2 dataset demonstrate state-of-the-art performance across multiple metrics, including the newly proposed $DET_p$ metric for endpoint detection. Ablation studies show consistent improvements from each module.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n* Results on OpenLane-V2 are consistently strong, outperforming prior works such as TopoLogic and TopoFormer across the sub-tasks.\n* The improvements brought by each component of TopoPoint are well-documented in the ablation study.\n\n**Weaknesses**\n* The use of a fixed number of queries could limit adaptability in dense or highly dynamic traffic scenes, as noted in the paper.\n* As acknowledged in the checklist, all results are based on single experiment runs without variance estimates.\n* The method is evaluated exclusively on data from Argoverse and nuScenes (U.S. and Singapore) under primarily favorable weather. This leaves open questions about performance under adverse conditions or in new geographical domains.\n\nQUESTIONS:\n* Given the complexity of TopoPoint, can the authors provide runtime benchmarks? Is the system deployable at real-time rates?\n* Have the authors considered evaluating TopoPoint on datasets with different geographic or environmental conditions (e.g., KITTI, DENSE)? Qualitative or quantitative comparisons on such datasets would provide insights on the robustness of TopoPoint.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on the topology reasoning task in autonomous driving. Previous methods first predict independent lanes and then use a topology head to generate a lane-lane adjacency matrix, but they suffer from endpoint deviation (or inconsistent endpoints). To address this, the authors propose jointly predicting lanes and endpoints, then matching lanes to shared endpoints to ensure consistency. Experiments on the OpenLane-V2 benchmark demonstrate that the proposed method outperforms SOTA methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The idea of introducing endpoints to improve lane topology reasoning is both intuitive and effective.\n2. The proposed method achieves state-of-the-art performance on the OpenLane-V2 benchmark.\n3. Ablation studies demonstrate that the proposed components (point-lane attention, point-lane GCN, and geometry matching) each contribute to improving OLS performance.\n\nWeaknesses\n1. The authors use high-resolution front-view images while downsampling other views by 0.5×, which alone improves OLS from 43.4 to 46.0. To isolate the impact of endpoint detection, this strategy should either be removed or applied uniformly to SOTA methods. Otherwise, TopoPoint's gains may be attributed to this resolution trick rather than topology reasoning.\n2. Despite superior topology metrics, TopoPoint lags behind TopoFormer in DET_l. The author should explain why the proposed method can no achieve better lane detection performances.\n\nQUESTIONS:\n1. Conduct more fair comparison by removing the image scale trick or adding this trick to SOTA methods.\n2. The authors should analyze why TopoPoint underperforms TopoFormer in DET_l (lane detection accuracy). Can TopoFormer's architecture benefit from the proposed endpoint strategy to achieve comparable gains?\n3. What about adopt PLGM to baseline methods? If lane detection is sufficiently accurate, the Point-Lane Geometry Matching (PLGM) module could be independently validated on baseline methods (without endpoint detection) to assess its standalone contribution to topology reasoning.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes a topology reasoning method in Bird’s-Eye View (BEV). The core idea is to address the problem of *diverged endpoints*—where lanes that should share the same endpoints are predicted as having different endpoints. The authors introduce corresponding network designs, including point query, point-lane decoder, and geometry matching, which are technically sound.

STRENGTHS AND WEAKNESSES:
The quality of this work is high. The paper is well-written and easy to follow. The proposed design effectively tackles topology reasoning challenges and holds significant value. The originality of the approach is relatively strong.

QUESTIONS:
Although this method achieves competitive performance compared to existing approaches, the accuracy of HD topology reasoning remains far from practical application. I would like to hear the authors’ thoughts on the following considerations:  

1. **Data Sufficiency Hypothesis**: Are current methods theoretically sufficient for HD topology reasoning, with performance limitations primarily due to insufficient training data? Would more data alone resolve these issues?  
2. **Inherent Limitations Hypothesis**: Even with abundant data, might current methods still fail to fully address the problem, suggesting fundamental limitations in the approach?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03