TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Authors: Dongming Wu
Jiahao Chang
Fan Jia
Yingfei Liu
Tiancai Wang
Jianbing Shen
Venue: ICLR 2024 poster (2024)
Keywords: Autonomous Driving
Driving Topology Understanding
Primary area: representation learning for computer vision, audio, language, and other modalities

Abstract: Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, \textit{i.e.}, lane-lane topology, and lane-traffic topology. In this work, we first present that the topology score relies heavily on detection performance on lane and traffic elements. Therefore, we introduce a powerful 3D lane detector and an improved 2D traffic element detector to extend the upper limit of topology performance. Further, we propose TopoMLP, a simple yet high-performance pipeline for driving topology reasoning. Based on the impressive detection performance, we develop two simple MLP-based heads for topology generation. TopoMLP achieves state-of-the-art performance on OpenLane-V2 dataset, \textit{i.e.}, 41.2\% OLS with ResNet-50 backbone. It is also the 1st solution for 1st OpenLane Topology in Autonomous Driving Challenge. We hope such simple and strong pipeline can provide some new insights to the community. Code is at https://github.com/wudongming97/TopoMLP.

Published as a conference paper at ICLR 2024
TOPOMLP: A SIMPLE YET STRONG PIPELINE FOR DRIVING TOPOLOGY REASONING
Dongming , Jiahao C Fan Jia Yingfei Liu", Tiancai Wang" , Jianbing Shen
1 Beijing Institute of Technology. 2 Uniiersstty of Science and Technology of China, 3 MEGVII Technology. SKLLIOOTS, University of Macau (wudongming97, shenjianbingcgl/ gmail com, wangtiancal Emegvii.
ABSTRACT
Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, i.e., lane-lane topology, and lane-traffic topology. In this work, we first present that the topology score relies heavily on detection performance on lane and traffic elements. There- fore, introduce powerful 3D lane detector and an improved 2D traffic element detector to extend the upper limit of topology performance. Further, we propose TopoMLP,, a simple yet perfopperformncce pipeline for driving topology reasoning. Based on the impressive detection performance, we develop two simple MI.P- based heads for topology generation. TopoMLP achieves state-of-the-art perfor- mance on OpenLane-Ve V2 dataset, i.e., 41.2% OLS with ResNet-50 backbone. It is also the 1st solution for 1st OpenLane Topology in Autonomous Driving Chal- lenge. We hope such simple and strong pipeline can provide some new insights to the community. Code is at httpss///thibbbbooo///ww/gnnng//
INTRODUCTION
Understanding the topology in road scenes is an important task for autonomous driving, since it provides the information about the drivable region as well as the traffic signal. Recently, the topol- ogy reasoning task has raised great attention in the community thanks to its crucial application in ego planning (Chai et al.) 2020 Casas et al., 2021 Hu et al. 2023). In specific, given multi-view images, topology reasoning aims to learn vectorized road graphs between the centerlines and traffic elements (Li ct al., 2023 Wang et al. 2023). consists of four primary tasks, centerline detection, traffic element detection, lane-lane topology, and lane-traffic topology reasoning.
Different from the conventional perception pipelines that include multiple independent tasks (Li et al. 2022b Liu et al.) 2023b), these four tasks naturally have logical order, i.e., first-detcttthhen- reason. If some lane and traffic instances are not detected, the corresponding topology connection will be missed, as illustrated in the right of Fig- [ It naturally leads to question: What is the extent of the quantitative effect of basic detection on topology reasoning? To answer this question, we conduct detailed ablation studies on detection performance by varying the backbones. It shows that the topology performances are constantly improved with stronger detection. When the basic detec- tion is freezed, we find that replacing the topology prediction with the ground truth (GT) introduces minor improvements. For example, when using Swin-B B backbone, the TOP,, and TOP scores with topology GT are 10.0% and 30.9%. which are only higher than using topology prediction by 0.5% and 2.6%, respectively. This phenomenon encourages us to prioritize the design of two detectors.
In specific, we employ two query-based detection branches: one (Liu et al. 2023b) dedicated to the detection of 3D centerlines, and another one (Zhu et al. (021) for 2D traffic detection. The 3D lane detector utilizes a smooth lane representation and interprets each lane query as a set of control points of Bézier curve. Inspired by MOTRy2 (Zhang et al. 2023). the performance of 2D traffic
"hii work was done during internship at MEGVII. *Project leader. *Corresponding author: Jianbing Shen. This work was supported in part by the FDCT grants 0102/2023/RAA2, 0154/2022/43 and 001/2024/SK1,, the MYRG-CRG2022-00 001022000011111000 3-IOTSC-iCT grant and the SRG2022-0002--OTSSS grant.

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Published as a conference paper at ICLR 2024
Backbone
DET;
DET,
Topo GT
TOPı
TOPi.
OLS
7.2
22.8
38.2
ResNet-50
28.3
50.0
7.5 (+0.3)
24.4 (+1.6)
38.3 38.3 (+0.1)
7,9
25.6
40.1
VOV
29.7
52.1
8.5 (+0.6)
27.5 (+1.9)
40.8 (+0.9)
28.3
Swin-B
30.7
54.3
10.0 (-0.5)
30.9
(+2.6)
43.1 (+0.9)
Figure 1: Left: The evaluation of topology when using ground truth topology (represented as "Topo GT to replace predicted topology while retaining the detection results. Right: The illustration of how missing detections (represented by dashed lines) influence topology reasoning. They uncover an essential truth: the fundamental detections are paramount to the topology reasoning.
detector can be further enhanced by adding an additional YOLOv8 (optional) object detector thanks to its advantage on detecting small objects, such as traffic lights.
Despite the basic detection, another challenge in driving topology reasoning is how effectively
model the connection between lanes and traffic elements, Prior works (Langenberg et al., 2019 Can
al. 202T 2022 employ a straightforward method, which uses multi-layer perceptron (MLP)
to predict the topology relationship. However, they mainly focus on associating different lanes in image domain. To cope with the 3D space, some follow-up methods (Li al. 2023 Xu
2023)
tend to utilize graph-based modeling to predict topology structure.
In this paper, we develop simple yet effective framework, termed TopoMLP,, for topology reason- ing. Our work is inspired by the pairwise representation in human-object interaction detection (Gao) jet al., 2018 Chao et al. 2018; Wang et al.] 2019), similar to topology reasoning. The pairwise representation constructed by encoding the human/object pair boxes into two mask embeddings. These embeddings are concatenated together and further used to perform action classification by a simple MLP. We wonder if it is possible to develop simple MLP-based framework for sufficiently understanding the relationships in driving topology reasoning. Taking lane-lane topology as an ex- ample, if the lanes are accurately predicted, the intersection points (see Fig. 2 between lanes can be easily reasoned to he overlapped. As for the lane-traffic topology, the traffic elements can be easily matched with the corresponding centerlines by the relative location between traffic bounding boxes and lane points. Therefore, a simple MLP seems enough for efficient topology reasoning. Specifically, we convert the query representations of both traffic elements and centerlines into two embeddings and concatenate them together for topology classification by an appended MLP.
Moreover, we notice that the topology metrics of OpenLane- V2 have some drawbacks. It uses graph- based mAP, while it focuses more on the order of predictions. Some false positives from unmatched lanes or traffic elements are defaulted to a high confidence score, i.e., 1.0. Accordingly, manually decreasing the priority of these false positive predictions (or increasing the priority of true positive predictions) enables to improve the overall mAP score by a large margin. To tackle this problem, we suggest to include correctness factor based on existing topology metric to correct the drawback.
Our contributions are summarized as four-fold. First, we provide an in-depth analysis of the na- ture of driving topology reasoning. It requires following better topology prediction. Second, we propose a simple but strong model, named TopoMLP. It in- cludes two well-designed nigh-performance detectors and two elegant MLP networks with position embedding for topology reasoning. Third, we claim that the current topology reasoning evaluation possesses significant loophole. To rectify this, we enhance the topology metric by incorporating correctness factor. Fourth, all experiments are conducted on the popular driving topology reason- ing benchmark, OpenLane-V2. showing TopoMLP reaches state-of-the-art performance. Besides, TopoMLP ranks 1st of 1st OpenLane Topology in Autonomous Driving Challenge (Wu et al. 2023
philosophy for
2 RELATED WORKS
2.1 LANE DETECTION METHOD
For long time, detecting lane markings has been one of the most important topics in autonomous driving. Prior works usually appearance and geometric cues to detect the road (Tan al. 2006
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Published as a conference paper at ICLR 2024
Alvarez & Lopez 2010 Paz et al. 2015). With the advancement of deep learning, the development of lane detection has made great progress. Among them, some methods attempt to use a segmen-
tation map to describe road lane (Batra et al. 2019 Can al. 2022 He & Balakrishnan 2022). Currently, rector-based methods have become mainstream because they can deal well with 3D lane detection (Garnett al. [2099] Guo et al. 2020; Yan al. 2022 Chen et al. (022). However, these methods base set of predefined Y-axis points in the query to predict 3D lanes, which fail to make the 3D lane prediction only across the Y axis. More recently, TopoNet (Li et al., (023) models each
lane into an anchor query, but it misses the lane prior with smoothed curve. In our study, we make full use of this prior to providing a smoother representation.
2.2 LANE TOPOLOGY LEARNING
Learning lane topology plays an important role in scene understanding for autonomous driving. Earlier works (Chu et al. 2019 Homayounfar et al. 2019 He et al. 2020 Bandara et al. 2022) focus on generating road graphs from acrial images. However, using aerial images is unreasonable for ongoing vehicles. Therefore, directly using vehicle-mounte sensors to detect lane topology has become popular due to their valuable application. STSU (Can et al. (02T] uses a Transformer-baa model to detect centerlines and objects together, and then predict centerline association formatted to a directed graph by an MLP. TopoRoad (Can et al. 2022) further introduces additional minimal cycle queries to ensure the preservation of the order of intersections. Can et al. (Can al. 2023) also
provide additional supervision of the relationship by considering the centerlines as cluster centers to assign objects and greatly improve the lane graph estimation. LaneGAP (Liao al. (023) designs a heuristic-basee algorithm to recover the graph from a set of lanes. CenterLineDet (Xu et al. 2023)
and TopoNet (Li et al., 2023) regard centerlines as vertices and design graph model update centerline topology. In this work, we focus on lane topology nature and employ a simple and elegant position embedding to enhance topology modeling.
2.3 HD MAP PERCEPTION
HD Map Perception aims to comprehend the layout of the driving scene, such as lanelines, pedestrian crossing, and drivable areas, mirroring the concept of driving scene reasoning. The recent research focuses on learning HD maps using segmentation and vectorization techniques to meet low-cost requirements. HDMapNet (Li al., (022a) explores grouping and vectorizing the segmented map with complicated post-processings. VectorMapNet (Liu et al., 2023a) directly uses a sequence of points represent each map element, further decoding laneline locations. Some follow-up methods propose different modeling strategies to represent the sequence of points, such as the permutation- based (Liao al. 20022,, the piecewise Bézier curve (Qiao et al.) [202)), the pivot-based map (Ding et al. (023). Different from the aforementioned approaches, our method employs simple and elegant modeling, each query referring to a lane.
3 METHOD
In this section, we elaborate on TopoMLP, a unified query-based framework for driving topology reasoning. is able effectively accomplish four different tasks in single framework, including lane detection, traffic element detection, lane-lane topology, and lanc-traffic topology prediction. The overall pipeline ToPoMI. shown in Fig. More details are described as follows.
3.1
LANE DETECTOR
Our lane detector inspired by the advanced 3D multi-view object detector PETR (Liu et al. 2022 2023b), which first introduces 3D position embedding (3D PE) into the query- based framework DETR (Carion et al., 2020 Zhu et al. 2022).. In this work, we represent each centerline as a smooth Bézier curve with M control points within 3D space and each curve refers to lane query. Our lane detector performs direct interaction between lane queries with multi-view visual features in transformer decoder and outputs control points, further transformed to lane coordinates.
Formally, given multi-view images from camera sensors, we first employ a backbone (e.g., ResNet- 50 (He al. 2016)) to generate feature maps F XVXCxHxW where V, C, H, and W represent
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Published as a conference paper at ICLR 2024
Concatenniio
Linear Layer
Addition
Topology
Optional
Traffic Decoder
1.I Topology
F
VOL0v 8
Tnatic Faaue
Eaxe Femera
Lane Decoder
LL Topology
cuab Epokts
Lazz
Pionees
The overall architecture TopoMLP. The lane decoder depicts each centerline as Bézier curve for smooth representation. The traffic decoder is optionally enhanced by additional YOLOv8 proposals. The prediction of lane-traffic (LT) and lane-lane (LL) topology is accomplished by an MLP with position embedding. "Topology" means an operation in 3.3
Figure 2:
the view number, channel, height, and width of the features, respectively, The 3D PE is encoded into the visual features to generate position-aware features following (Liu al.) 2022). Then we initialize Nr learnable 3D lane anchor points, denoted as Q' E R2 After projecting the feature dimension of anchor points from 3 to C using position encoding and linear layer, we further feed it into transformer decoder to update the lane query features Q":
Q' T LaneDecoder( F, Linear(Q*)) E N.XC
where the LaneDecoder is stack of Transformer decoder layers. On top of the transformer decoder, we adopt two independent MLPs to predict the offset of control points and the classification scores, respectively. The final control point outputs ordered and obtained by adding basic anchor points with the relative offsets. The control points are transformed into lane points for training and testing.
3.2 TRAFFIC ELEMENT DETECTOR
The prevalent approaches for traffic element detection in driving topology reasoning mainly query-based and end-to-end deployed (Li et al., 2023 Kalfaoglu et al. 2023 Lu et al. 2023). Al- though such straightforward end-to-end implementation is appealing, the detection performance is much inferior to the specialized 2D detectors, such as YOLO series, due to small objects and class imbalance problems. To address these limitations, we propose optionally improve the query-based detectors by elegantly incorporating an extra object detector YOLOv8.
Our traffic element detector typically follows the head design in Deformable DETR ZZuu et al. 2021) to predict bounding boxes and classification scores. It adopts query embeddings to generate set of reference points as anchors. We modify the reference format into reference boxes with the center points, height, and width. As alternative, the high-quality proposals from YOLOv8 can serve as an anchor box initialization, providing better local priors. It greatly eases the trade-off between topology reasoning and traffic detection.
Specifically, we first collect the multi-scale feature maps the front view from multi-view features F, denoted F9. YOLOv8 takes ED as input and generates multiple proposals, which are con- catenated with a set of reference boxes produced from randomized queries, denoted as R". The generated boxes by YOLOv8 which are concatenated with the randomized queries, denoted as QT. The query features as well as the reference boxes are fed into the deformable decoder:
encoded by sine-cosine embedding to generate query features,
T TrafficDecoder F R","") RR
where the TrafficDecoder is stack of Deformable decoder layers. Based the decoded traffic features QT. implement two independent MI.Ps for bounding box classification and regression.
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Published as a conference paper at ICLR 2024
3.3 LANE-LANE TOPOLOGY REASONING
Lane-lane topology reasoning branch aims to predict the lane-lane connection relationship. To in- corporate the discriminative lane information, integrate the predicted lane points into the lane query features. In specific, we implement MLP to embed the lane coordinates and then add them into the decoded lane query features Q' E RNxx" For notion simplicity, we still use Q' to repre- sent the integrated query features. They are repeated Nz. times, generating two features with sizes (N,, xCC xC and (Nr,)xN,,XC where (N₂, defines different repeating refeatin directions. After concate-
nation operation generating Q'. R x X2C
apply MLP to perform binary classification: GLL = MLP((**) R
(3)
where the GLLL is lane-lane topology prediction.
3.4
TRAFFIC TOPOLOGY REASONING
The key idea of our lane-traffic topology reasoning is to project two kinds of features into the same space. Given the lane query embedding Q E RN,xC from 3D space, we sum the view transfor- mation matrix A E R3x3 from 3D to perspective view with it, Le.. Q' + MLP(A). Here, the view transformation matrix A is formulated in terms of camera intrinsic and extrinsic. Similar to lane- lane topology, the transformed lane query features and the traffic query embedding Q" E N XC are transformed into Q E RRNxNN x2C through repeating and concatenating operations. An MLP network is used to generate lane-traffic topology prediction G
G LT MLP((UT) CRN.N....
(4)
3.5
Loss FUNCTION
Our final loss function is defined as follows:
L = Ldet, + Later, + Lropu + Lrapre
(5)
where Ldet, is lane detection loss, which includes focal loss (Lin et al. 2017) supervising classifi- cation and an LI loss for lane regression. Ldet, is traffic element detection loss, which has a focal loss for classification, L1 loss and GloU loss for bounding box regression. The lane-lane topol- ogy loss Leopu contains a focal loss for binary classification and an LI loss between the matched lane points in terms of the topology ground-truth. The lane-traffic topology loss Lrop,, is focal loss for binary classification. Since our TopoMLP is a query-based method, it requires the matching between the predictions and ground-truth. In this work, we only use bipartite matching on the basic lane and traffic element detection. The matching is directly used in topology reasoning loss as well.
4 EXPERIMENTS
4.1 DATASET AND METRIC
Dataset. The experiments are conducted on the OpenLane-V2 V2 (Wang et al., 2023). OpenLane- V2 a large-scale perception and reasoning dataset for scene structure autonomous driving. It has two subsets, i.e. subset_A _A and subset_B, developed from Argoverse 2 (Wilson et al., 2021) and nuScenes (Caesar et al. 2020), respectively. Each subset comprises 1,000 scenes with annotations at =.H Note that the subset_A A contains seven views and subset_ contains six views.
Evaluation Metric. These two basic detections require measuring instance-level performance. Therefore, the perception metrics, including DET, and DET,, are mean average precision (mAP) following the work (Wang et al. 2023). Specifically, DET, employs the Fréchet distance for quan- tifying similarity and is averaged over match thresholds set at (1.0, 2.0, 3.0). On the other hand, DET, employs Intersection over Union (IoU) as the similarity measure, with averages calculated over various traffic categories. For topology metrics, the TOP score also employs mAP metric, which is designed specifically for graph data. To summarize the overall effect of primary detection and topology reasoning, the OpenLane-Vi Score (OLS) conducted as:
OLS DET. + DET, + /(TOPa) + [(TOP..)].
(6)
where f is the square root function.
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Published as a conference paper at ICLR 2024
Method
Backbone
Epoch
DET,
DET;
TOP
TOP
OLS
STSU (Can et al. 2021
ResNet-50
24
12.7
43.0
0.5
15.1
25.4
VectorMapNeet Liu
2023a
ResNet-50
24
11.1
41.7
0.4
5.9
20.8
MapTR (Liao et al 2022
ResNet-50
24
17.7
43.5
10.4
26.0
TopoNet (i et al.
2023
ResNet-50
24
28.5
48.1
4.1
20.8
35.6
TopoML.P
ResNet-50
24
28.3
50.0
7.2
22.8
38.2
TopoML
ResNet-50
24
28.8
53.3
41,2
TopoMLP
VOV
24
29.7
52.1
7.9
25.6
40.1
TopoMLP
Swin-B
24
30.7
54.3
9.5
28.3
42.2
TopoMLPP
Swin-B
24
30.0
55.8
9.4
31.7
43.3
TopoMLP
Swin-B
48
32.5
53.5
11.9
29.4
43.7
Table 1: Performance comparison with state-of-the-art methods on OpenLane- V2 subset_A -esisstt_sse set.
Results for existing methods are from TopoNet. TopoMLP trained end-to-end, while *** indicates using extra YOLOv8 proposals. The best is in bold.
Method
Backhone
Epoch
DET,
DET;
TOP
TOP
OLS
STSU (Can al. 2021
ResNet-50
24
8.2
43.9
0.0
9.4
21.2
VectorMapNet (Luu et al.
2023a
ResNet-50
24
3.5
49.1
0.0
1.4
16.3
MapTR (Liao al.
2022
ResNet-50
24
15.2
54.0
0.5
6.1
25.2
TopoNet
et al.
2023
ResNet-50
24
24.3
55.0
2.5
14.2
33.2
TopoMLP
ResNet-50
24
26.6
58.3
7.6
17.8
38.7
TopoMLP TopoMLP
VOV
24
29.6
62.2
8.9
20.5
41.7
Swin-B
24
32.3
65.5
10.5
23.2
44.6
Table 2: Performance comparison with state-of -ofe--o-thhhaarrr methods on OpenLane- V2 subset set. Results for existing methods are from TopoNet.
4.2 IMPLEMENTATION DETAILS
Feature Extractor. All images are resized into the same resolution of 1550xx048, and are down- sampled with ratio of 0.5. We implement different backbones, i.e., ResNet-50 (He et al.) (2066), VOV (Lee et al., 2019). and Swin-B (Liu et al. (02T) for feature extraction. The number of output channels set to C= 256. For lane detection, the C5 feature is upsampled and fused with C4 feature using FPN. For traffic detection, the C3, C4, and C5 features are used as the feature pyramid.
Lane Detector. The lane query number is set to N₂ 300, and the number of control points is 4. During training, the control points are transformed into 11 lane points for calculating loss. We set the region to [-51.2m. 51.2m] the X-axis, [-25.6m. 25.6m] the Y-axis, and [-8m, 4m) the Z-axis. The lane detection head is composed of 6 transformer decoder layers. The MLP heads contain two fully connected layers with ReLU activation. For lane detection loss Last, the weight of the classification part is 1.5, and the weight of the regression part 0.2.
Traffic Detector. The decoder architecture follows the original designs of Deformable DETR (Zhu et al.] 2) The number of random queries in the traffic decoder is 100. The detection results from YOLOv8 are stored advance. The weight of the classification loss is 1.0, the weight of LI loss is 2.5, and the weight of GloU loss is 1.0.
Topology Head. The MLP network used in topology heads consists of three linear layers with ReLU activation. We represent the lane-lane topology loss with L1 loss and classification loss as Cropn CL1 + Aeteholes where ÅLι 0.1 and Acts 5. The loss coefficient of lane-traffic topology loss Liopn is 0.5.
Training and Inference. The overall model TopoMLP is trained by Adam optimizer (Loshchilov & Hutter 2017) with a weight decay of 0.01. The learning rate is initialized with 2.0 10 and decayed with cosine annealing policy (Loshchilov & Hutter 2016). We adopt the HSV augmentation and grid mask strategy for training. All the experiments are trained for 24 epochs on 8 Tesla A100
Thee official codes adopt available http:///iitnakcmmmmiimasssssssis

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Published as a conference paper at ICLR 2024
Figure 3: Qualitative results for lane detection and lane-lane topology of TopoMLP. Given the multi-view images, our method predict the most lanes and connect them correctly under various challenges, like occluded lanes and complicated intersections. The green lanes are ground-truth and the red lanes predictions, which are projected into images and BEV map.
GPUs with a batch size of 8 if not specified. During the inference time, our model outputs at most 300 lanes for evaluation. Other post-processing techniques are not implemented.
4.3 STATE-OF-THE--RT COMPARISON
We compare TopoMLP with the sfate-of-the-art approaches, such STSU (Cnn al. 202 Vec- torMapNet (Liu et al. 2023a), MapTR (Liao et al. 2022), TopoNet (Li et al. 2023). Table shows the results subset A of OpenLane-V2. V2. Without bells and whistles, our method achieves 38.2 OLS using ResNet-50 backbone, surpassing other state-of-the-art methods. Compared to TopoNet,
approach shows much better topology reasoning accuracy (7.2 v.s. 4.1 TOPn, 22.8 v.s. 20.8 on TOP;)) while also achieves decent detection accuracy (28.3 v.s. 28.5 on DET, 50.0 v.s. 48.1 on DET,). For better performance, we apply more powerful backbone and more training time: when using Swin-B for training 48 epochs, the OLS score rises to 43.7.
Table 2 shows the performance comparison on OpenLane- V2 subset _B Our proposed TopoMLP exceeds other models in all metries when using the same ResNet-50 backbone. Particularly in terms of topology performance, surpasses TopoNet by a large margin (7.6 v.s. 2.5 on TOPu, 17.8 v.s. 14.2 on T)P;)). Moreover, the performance boost is also observed when integrating more powerful backbones. Overall, these results significantly highlight the efficacy of our TopoMLP model.
4.4 ABLATION STUDY
In this section, study several important components of our method and conduct ablation experi- ments on OpenLane- V2 subset_A.
Analysis Lane Detection. We investigate the effect of different settings in lane detection. i) In Table 3 (a), the improvement in lane detection and lane-lane topology performance is clear when the lane query increases from 200 300. However, it is observed that any further increase does not contribute to additional improvement. To balance the model efficiency and performance, the number of lane query set to 300. ii) 3 illustrates the influence of control point in Bézier modeling. Empirically, we choose 4 control points for better performance.
YOLOv8 Proposal on Traffic Detection. To study the benefit of using YOLOv8 proposals, test its effect under two settings: using ResNet-50 and Swin-B backbone. The main results are shown in Table marked by "** It is well seen that using YOLOv8 predictions as proposal queries consistently improves the detection performance, indicating the effectiveness of YOLOv8 proposals. Moreover, is worth noticing that TopoMLP without YOLOO88 still achieves higher traffic detection scores than other counterparts.
Representation Way in Topology Reasoning. is also of interest to analyze the impact different lane and traffic element representation methods in topology reasoning. i) We first analyze the lane representation in lane-lane topology, which additionally integrates lane coordinates. As shown in (c), removing it leads minor performance degradation TOP which indicates that the explicit lane position is useful for topology reasoning. Moreover, abandoning LI loss for intersection point supervision also causes score decrease. ii) Table 3 (d) explores the impact of incorporating

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Prediction
Ground- Truth
Prediction
Ground-Trull
Figure 4: Traffic detection and lane-traffic topology from TopoMLP. Our method can detect the traffic elements in the front view and associate them with lanes, The green represents GT, while the red means prediction. Traffic predictions are grounded by different colors in terms of category.
Lane Queries DET;
DET,
TOPn
TOP;
OLS
Control Points DET;
DET,
TOPn
TOP:
OLS
200
28.2
49.9
6.1
20.2
36.9
3
26.6
49.9
7.0
21.5
37.3
300
28.3
50.0
7.2
22.8
38.2
28.3
50.0
7.2
22.8
38.2
500
27.9
49.6
7.3
22.4
38.0
5
27.8
48.5
6.6
21.5
37.1
Different number of lane queries.
(b) Different number control points.
LL Topo
DET,
DET;
TOPa
TOP;
OLS
LT Topo
DET, DET, TOPn
TOP; OLS
Ours
28.3
50.0
7.2
22.8
38.2
Ours
28.3
50.0
7.2
22.8
38.2
w/o position|
27.9
50.9
6.9
21.6
37.9
w/o transform
28.4
49.3
7.2
21.4
37.7
w/o 1.1 loss
26.6
50.9
6.5
22.1
37.5
w only box
28.2
49.6
7.1
22.0
37.8
(c) LL Topo is the short name for lane-lane topol- ogy. " w/o position" means removing lane coordi- nate embedding. "w/e LI loss" means removing the supervision of interaction points.
(d) LT Topo is the short name for traf-traffic topology. "transform" means using view transfor- mation matrix lane feature. "only box" means using bounding hox as traffic representation.
Table 3: The ablation studies of different components in the proposed TopoML... The experiments are conducted on OpenLane-V A. We bold the best scores.
the view transformation matrix into the lane feature for lane-traffic topology. It suggests that the integration of this matrix into lane representation improves the reasoning of lanc-traffic topology (22.8 v.s. 21.4 on TOP:). iii) Using the bounding boxes of traffic elements to replace traffic features results in a drop on TOP; (22.8 v.s. 22.0), as shown the last row of Table 3 (d). This is because only adopting boxes lacks category information. Despite the advantages of position embedding, single MLP network proves sufficient for achieving high-performance topology reasoning.
4.5 VISUALIZATION
We visualize the lane detection and lane-lane topology reasoning results in Fig. 3 by projecting 3D lanes into images. Despite potential challenges like intricate intersections occluded centerlines, TopoMLP well predicts the centerlines and constructs a lane graph in BEV. Fig. dissplay the results of traffic detection as well lane-traffic topology reasoning. As clearly shown, TopoMLP identifies the majority of traffic elements, even small objects, and allocates them to the appropriate lanes.
4.6 MORE DISCUSSION
Before stepping into our thorough analysis, let's revisit the definition the topology metric. Given the ground-truth graph G = (V, E) and the predicted one &= (V, É), we establish a projection on the vertices such that V.CV This projection utilizes the Fréchet and IoU distances to measure similarity among lane centerlines and traffic elements respectively. Within the predicted V', we consider two vertices as being connected if the confidence of the edge surpasses 0.5. Subsequently, the TOP score derived by averaging vertice mAP between (V, E) and (V', É') overall all vertices:
EnEN P(fi')cconttion (ńi N()))
TOP Σ
[N())
NEV
8

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Published as a conference paper at ICLR 2024
Unmatched
Matched
Ordered
Precision
0.9
0.1
0.6
0.9
0.1
Calculation
0.33
0.5
0.6
Enhance Prediction
Ordered
Precision
Calculation
0.67
0.5
0.6
Figure 5: The illustration of loophole TOP metric. Enhancing the prediction scores leads to
true positives prior to some false positíves from unmatched instances, further improving precision.
Original Prediction
Enhanced Prediction
Method
DET;
DET:
TOP
TOP
OLS
TOPμ
TOP&
OLS
TopoNet (Li al. 2023
27.1
44.8
4.0
20.6
34.4
11.5(+7.5)
21,6(+1.5 1.0)
38.1(+3.7)
MopoMLPTOUnm
28.3
50.0
7.2
22.8
38.2
19.0(+1188)
23.4(+0.6)
42.2(+4.0)
Method
DET;
DET;
TOP
TOP
18
OLSt
TOPÍ;
TOP
OL.t
TopoNet [i al. 2023
27.1
44.8
20.4
32.8
1.0
20.9
32.0
TopoMLPTOUr))
28.3
50.0
4.5
22.1
36.3
1.9
22.5
34.5
Table 4: The comparison on the original TOP metric and our adjusted TOP (marked by t) when using enhanced prediction or not. TopoNet reimplemented by ours using the same backbone ResNet-50 with our ToPoMLP. The experiments are conducted on OpenLane-V2 subset
where N(v) is the ordered list neighbors vertex e ranked by confidence and P(v) is the preci- sion of the i-th vertex in the ordered list.
We provide toy example the important loophole in Fig. crucial point hinges on the precision of the ordered list. For those unmatched instances that our detector cannot identify, their confidence scores are defaulted to 1.0. That is, there are lots of false positives with high confidence. Suppose push the prediction confidence into 1.0/0.0 in terms of 0.5, our prediction with true positives will have higher confidence, hence, leading to enhanced precision. The quantitative results are shown in the first two rows of Table Using this strategy enhance prediction leads consistent performance improvement, including TopoNet and our method TopoMLP.
To tackle this issue, we suggest novel TOP metric incorporated with a correctness factor. Let's symbolize the enhanced precision as P(u)t. The adjusted TOP metric TOP formulated as:
Nrp
EveR( 1 E N(v)) (NrP + NPP)
TOP*
(8)
[[(e]
where Nyp is the number of true positives and NFp is the number of false positives. In the last two rows of Table we evaluate TopoNet and TopoMLP on the adjusted topology metric, demonstrating its capability to effectively shield against the "attack". Despite the alteration in the metric, TopoMLP still surpasses other methods such as TopoNet performance.
CONCLUSION
In this paper, we propose simple yet strong pipeline for driving scene topology, named TopoMLP. starts a significant observation that the reasoning performance is limited by the detection scores. Therefore, we first focus on designing two powerful detectors for 3D lane detection and 2D traffic detection, respectively. As for topology reasoning, combining the appreciated position embedding and elegant MLP network is enough to achieve impressive performance. TopoMLP is the 1st solution for 1st OpenLane Topology in Autonomous Driving Challenge. We hope our work opens up new insights into exploring driving topology reasoning.

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Published as a conference paper at ICLR 2024
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12


Reviews:
[{"review_id": "8kTZlLWDjT", "reviewer": "Reviewer_MJLZ", "summary": "The authors introduces TopoMLP driving topology reasoning using vehicle-mounted sensors.\nThe model starts with two strong decoder to detect the lanes and the traffic elements.\nFor the lanes, the model uses Bezier curves as lane queries for 3D centerline position embedding.\nFor the traffic elements, it uses proposals from YOLOv8 and the corresponding multi-scale features, and feed them to a series of deformable decoder layers.\n\nThese information then are passed to two MLP network reasoners to perform the final lane-lane and lane-traffic-element topology reasoning.\n\nBoth of these modules apply pair-wise representations for their respective inputs.\nHowever, Lane-Traffic reasoning also takes in camera intrinsic and extrinsic information.\n\nThe loss functions uses focal loss throughout, for classification for both lane and traffic detection, and lane-lane and lane-traffic topology. \nIn addition, lane detection loss also includes L1 loss for lane regression and lane-lane topology, while traffic detection loss also includes GIoU loss for bounding box regression.\n\nTopoMLP is tested on OpenLane-V2 benchmark, 2000 scenes in total, against a number of STOA techniques.\nThe model is able to surpass the rest of the techniques, by showing a much better topological reasoning capability, while achieving a decent detection accuracy.\nThe ablation study shows that the value of using YOLOv8 object detection, explicit lane coordinates, and injecting transformation matrix.\nFinally, the paper proposes an enhanced TOP score incorporating correctness factor, which allows for more accurate precision.", "questions": "Would you be able to ground the TOP numbers with what is required for autonomous driving. \nAlso discussions on what is not yet solvable.\n\nTypos, Grammar, etc.:\npg. 4, figure 2 caption: “Topoogy” —> “Topology”", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed model improves the STOA on OpenLane dataset by refining its various components.\nThe proposed enhanced TOP score also shed lights to a more accurate evaluation.", "weaknesses": "It is hard to make sense of why the TOP numbers are quite low, especially lane-lane topology.\nIt would be good to add what is an acceptable number for robust driving. \nThere is not a lot of discussion of what is still not solvable, to allow readers to understand the shortcomings of the methods. \nThis would add discussions on what is the next steps to take.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yDmaiabuDB", "reviewer": "Reviewer_tnze", "summary": "The paper proposes a simple and strong method called TopoMLP, on the openlane-v2 benchmark for topology reasoning in the driving scenarios. Authors claim that the key insight is to improve the detection part in order to secure a steady improvement of the topology score. This is also the winner solution for the Openlane-v2 challenge.", "questions": "1. I see the pipeline resembles to some extent with TopoNet method, where two branches are capable of recognizing the traffic lights and lane topoploy respectively. Using two simple MLP for the head is the core contribution I suppose. Is there any ablations to verify that using some complicated design (like a Transformer head) could \"downgrade\" the OLS score?\n\n2. Section 4.6 is a good contribution to the open-sourced community. Do authors communicate with organizers of the Challenge to reconsider the metric and maybe the leaderboard would be refreshed? On further consideration, the current OLS is a general metric. Is it good to design a separate metric to evaluate the topology performance at intersections (which is crucial I suppose)?\n\n3. This work generates the centerline of the lane, which is not the physical representation of the road scene. Is it a good formulation? On a higher level, I think the current form of Openlane-v2 tackles only partial challenges in the static road recognition. How to utilize the topoloy to faciliate downstream task? This could potentially trigger more future research in related domains.\n\n----\nMinor\n\n1. Topo in Figure 2. \"Topoogy\"\n2. G^LL in Equation 3 is the operation of Topology in Figure 2? I suggest to align the concept with equation and the figure.\n3. How to balance the different weights in Equation 5 of different branches? Are there some ablations or just equally distributed?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "In general the paper carries some good merits, including:\n\n+ the paper is easy to follow. The contributions are very clear: good detection increases the topology reasoning. Based on the detection module, using two simple MLP heads for topology generation could win among other approaches.\n\n+ this work derives from the participation of this year's Autonmous Driving Challenge. This is the first entry solution, as shown in Table 1 and Table 2. Authors find a problematic on the evalution metric of OLS score. Figure 5 is very illustrative. Using the rectified evaluation, the loophole could be remedied.", "weaknesses": "The paper has some obvious shortcomings as follows.\n\n- Motivation / technical novelty seems to be limitted. Following the second paragrah in the introduction, it is empirically found that topology performance is improved with stronger detection. Any insight behind this? There seems not too many discussions on it.\n\n- Some ablations and experiments need to be discussed. \n\nOverall this work falls into the borderline category without too many novelty or insight. However, given (a) the topology reasoning for lane detection is an emerging topic and there are few work in this direction, (b) the paper achieves the best result on the leaderboard of the Challenge, promised reproducible research to the community, the reviewer would slightly lean to encourage the manuscript to be polished in great extent.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1Djvk4HaLT", "reviewer": "Reviewer_bKsZ", "summary": "The paper presents a methodology named TopoMLP for topology reasoning in autonomous driving. Topology reasoning is essential for understanding road scenes and presenting drivable routes, requiring the detection of lane centerlines and traffic elements and reasoning their topology relationships, such as lane-lane and lane-traffic element topologies. The authors introduce a powerful 3D lane detector and an improved 2D traffic element detector, emphasizing the significance of detection performance on lane and traffic elements in influencing topology scores. They develop two simple MLP-based heads for topology generation, based on impressive detection performance. The proposed model, TopoMLP, achieves state-of-the-art performance on the OpenLane-V2 benchmark and ranks 1st in the 1st OpenLane Topology in Autonomous Driving Challenge. The authors also identify a loophole in the current topology metrics, and propose a correctness factor to enhance it.", "questions": "1. TopoMLP has a higher lane-lane topology score (7.2) compared to the previous state-of-the-art TopoNet (4.1) under the similar detection score. However, even without your specific designs, the performance of TopoMLP remains strong in the ablation study Table 3(c) (6.9 and 6.5). Can you explain the main reason for the strong TOP_ll performance of your model compared to TopoNet?\n2. Although the introduction of YOLOv8 can significantly improve performance, why not directly use YOLOv8's features for LT relationship inference, but use its detection results as proposals of a DETR?\n3. Can you clarify why the \"adjusted TOP\" dropped in the \"enhanced prediction\"? Do the true positives and false positives in N_TP and N_FP refer to the matching in detection or topology?\n4. Some errors in manuscript:\n   - In Figure 2, the transformation matrix A is added to the Traffic Features. However, in Section 3.4, it is mentioned that it is \"summed with the lane query embedding.\"\n   - In Page 6, Section 4.2, the subscript of \"The loss coefficient of lane-traffic topology loss\" should be \"lt\" instead of \"ll\".", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The authors conduct a comprehensive investigation into the significance of detection performance in topology reasoning, utilizing the OpenLane-V2 metrics. They highlight that with a detection score of 28.3, the topology score can only reach a maximum of 7.5, even if the topology reasoning results are identical to the ground truth.\n2. In the above scenario, their model achieves a topology score of 7.2. This demonstrates the model's superior performance in topology reasoning compared to other models evaluated using the OpenLane-V2 metrics.\n3. They propose using lane coordinate embedding and extra-supervision for interaction points to increasing LL topology reasoning performance. The LT topology can also benefited from view transformation embbeding and traffic element features.\n4. They have established a strong baseline framework for topology reasoning for autonomous driving, and plan to make it open-source, laying a solid foundation for subsequent work. They also pointed out a flaw in an existing metric, which has contributed to the advancement and refinement of this field.", "weaknesses": "1. Although the overall performance of TopoMLP is impressive, it primarily stems from the overall framework and training strategy, where the novelty being somewhat limited.\n2. The several modules proposed in the paper to enhance performance are effective, but they are quite straightforward and intuitive, lacking more refined and intricate design.\n3. Some parts of the manuscript are unclear and could be further improved; errors and suggestions for improvement are detailed later in the Questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0mxOsHj42E", "reviewer": "Reviewer_Cep6", "summary": "The paper presents a framework called TopoMLP for driving topology reasoning, which aims to accurately predict the relationship between lanes and traffic elements in autonomous driving scenarios. The key idea of TopoMLP is to leverage a simple yet effective approach based on MLP networks for topology reasoning. The authors also address the limitations of the topo metric of OpenLane V2 and provide an improved version. This topology reasoning algorithm achieves the state-of-the-art performance in the lane-lane and lane-traffic element relationship reasoning task on the OpenLane V2 datasets.", "questions": "1. Regarding the scenarios depicted in Figure 3, where the intersection area on both the left and right sides is either invisible or not directly observable, the displayed results appear to be very promising. I would like to confirm whether these results were obtained by overfitting the dataset.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The fundamental idea presented in the paper is solid, and the overall writing is of high quality.\n2. The paper provides experimental results that effectively support the proposed method.\n3. The authors enhance the query-based detectors by integrating additional reference boxes generated by YOLOv8.\n4. The topology reasoning method employed is both simple and effective.", "weaknesses": "1. One weakness of the proposed method is its inability to accurately represent complex shapes of map elements such as curbs using bezier curves. This limitation restricts the capability of the method for effective map learning and topology reasoning.\n2. Additionally, while the authors have made improvements to the query-based traffic elements detection model, the box proposals generated are still outputs from a yolov8 model, which leaves some aspects of the work incomplete.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)