Exploring Semi-Supervised Learning for Online Mapping

Online mapping is important for scaling autonomous driving beyond well-defined areas. Training a model to produce a local map, including lane markers, road edges, and pedestrian crossings using only onboard sensory information, traditionally requires extensive labelled data, which is difficult and costly to obtain. This paper draws inspiration from semi-supervised learning techniques in other domains, demonstrating their applicability to online mapping. Additionally, we propose a simple yet effective method to exploit inherent attributes of online mapping to further enhance performance by fusing the teacher's pseudo-labels from multiple samples. The performance gap to using all labels is reduced from 29.6 to 3.4 mIoU on Argoverse, and from 12 to 3.4 mIoU on NuScenes utilising only 10% of the labelled data. We also demonstrate strong performance in extrapolating to new cities outside those in the training data. Specifically, for challenging nuScenes, adapting from Boston to Singapore, performance increases by 6.6 mIoU when unlabelled data from Singapore is included in training.

Towards Accurate Ego-lane Identification with Early Time Series Classification

Accurate and timely determination of a vehicle's current lane within a map is a critical task in autonomous driving systems. This paper utilizes an Early Time Series Classification (ETSC) method to achieve precise and rapid ego-lane identification in real-world driving data. The method begins by assessing the similarities between map and lane markings perceived by the vehicle's camera using measurement model quality metrics. These metrics are then fed into a selected ETSC method, comprising a probabilistic classifier and a tailored trigger function, optimized via multi-objective optimization to strike a balance between early prediction and accuracy. Our solution has been evaluated on a comprehensive dataset consisting of 114 hours of real-world traffic data, collected across 5 different countries by our test vehicles. Results show that by leveraging road lane-marking geometry and lane-marking type derived solely from a camera, our solution achieves an impressive accuracy of 99.6%, with an average prediction time of only 0.84 seconds.

Bayesian Simultaneous Localization and Multi-Lane Tracking Using Onboard Sensors and a SD Map

High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle's 6D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.

Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It

Data leakage is a critical issue when training and evaluating any method based on supervised learning. The state-of-the-art methods for online mapping are based on supervised learning and are trained predominantly using two datasets: nuScenes and Argoverse 2. These datasets revisit the same geographic locations across training, validation, and test sets. Specifically, over $80$% of nuScenes and $40$% of Argoverse 2 validation and test samples are located less than $5$ m from a training sample. This allows methods to localize within a memorized implicit map during testing and leads to inflated performance numbers being reported. To reveal the true performance in unseen environments, we introduce geographical splits of the data. Experimental results show significantly lower performance numbers, for some methods dropping with more than $45$ mAP, when retraining and reevaluating existing online mapping models with the proposed split. Additionally, a reassessment of prior design choices reveals diverging conclusions from those based on the original split. Notably, the impact of the lifting method and the support from auxiliary tasks (e.g., depth supervision) on performance appears less substantial or follows a different trajectory than previously perceived. Geographical splits can be found https://github.com/LiljaAdam/geographical-splits

Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving

Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9x that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only large-scale AD dataset released under a permissive license, allowing for both research and commercial use. The dataset is accompanied by an extensive development kit. Data and more information are available online (https://zod.zenseact.com).

Performance Analysis and Robustification of Single-query 6-DoF Camera Pose Estimation

We consider a single-query 6-DoF camera pose estimation with reference images and a point cloud, i.e. the problem of estimating the position and orientation of a camera by using reference images and a point cloud. In this work, we perform a systematic comparison of three state-of-the-art strategies for 6-DoF camera pose estimation, i.e. feature-based, photometric-based and mutual-information-based approaches. The performance of the studied methods is evaluated on two standard datasets in terms of success rate, translation error and max orientation error. Building on the results analysis, we propose a hybrid approach that combines feature-based and mutual-information-based pose estimation methods since it provides complementary properties for pose estimation. Experiments show that (1) in cases with large environmental variance, the hybrid approach outperforms feature-based and mutual-information-based approaches by an average of 25.1% and 5.8% in terms of success rate, respectively; (2) in cases where query and reference images are captured at similar imaging conditions, the hybrid approach performs similarly as the feature-based approach, but outperforms both photometric-based and mutual-information-based approaches with a clear margin; (3) the feature-based approach is consistently more accurate than mutual-information-based and photometric-based approaches when at least 4 consistent matching points are found between the query and reference images.