We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they often overfit to specific domain biases, leading to suboptimal performance in various sensor setups and environments. Existing methods typically focus on adapting a single detector to the target domain, overlooking the fact that different detectors possess distinct expertise on different unseen domains. MS3D leverages this by combining different pre-trained detectors from multiple source domains and incorporating temporal information to produce high-quality pseudo-labels for fine-tuning. Our proposed Kernel-Density Estimation (KDE) Box Fusion method fuses box proposals from multiple domains to obtain pseudo-labels that surpass the performance of the best source domain detectors. MS3D exhibits greater robustness to domain shifts and produces accurate pseudo-labels over greater distances, making it well-suited for high-to-low beam domain adaptation and vice versa. Our method achieved state-of-the-art performance on all evaluated datasets, and we demonstrate that the choice of pre-trained source detectors has minimal impact on the self-training result, making MS3D suitable for real-world applications.
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and tested on another. We observe that lidar scan pattern differences form a large component of this reduction in performance. We address this in our approach, SEE-VCN, by designing a novel viewer-centred surface completion network (VCN) to complete the surfaces of objects of interest within an unsupervised domain adaptation framework, SEE. With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns. By adopting a domain-invariant representation, SEE-VCN can be classed as a multi-target domain adaptation approach where no annotations or re-training is required to obtain 3D detections for new scan patterns. Through extensive experiments, we show that our approach outperforms previous domain adaptation methods in multiple domain adaptation settings. Our code and data are available at https://github.com/darrenjkt/SEE-VCN.
Autonomous vehicles have the potential to lower the accident rate when compared to human driving. Moreover, it is the driving force of the automated vehicles' rapid development over the last few years. In the higher Society of Automotive Engineers (SAE) automation level, the vehicle's and passengers' safety responsibility is transferred from the driver to the automated system, so thoroughly validating such a system is essential. Recently, academia and industry have embraced scenario-based evaluation as the complementary approach to road testing, reducing the overall testing effort required. It is essential to determine the system's flaws before deploying it on public roads as there is no safety driver to guarantee the reliability of such a system. This paper proposes a Reinforcement Learning (RL) based scenario-based falsification method to search for a high-risk scenario in a pedestrian crossing traffic situation. We define a scenario as risky when a system under testing (SUT) does not satisfy the requirement. The reward function for our RL approach is based on Intel's Responsibility Sensitive Safety(RSS), Euclidean distance, and distance to a potential collision.
Recent Autonomous Vehicles (AV) technology includes machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. The research community and industry have widely accepted scenario-based testing in the last few years. As it is focused directly on the relevant crucial road situations, it can reduce the effort required in testing. Encoding real-world traffic participants' behaviour is essential to efficiently assess the System Under Test (SUT) in scenario-based testing. So, it is necessary to capture the scenario parameters from the real-world data that can model scenarios realistically in simulation. The primary emphasis of the paper is to identify the list of meaningful parameters that adequately model real-world lane-change scenarios. With these parameters, it is possible to build a parameter space capable of generating a range of challenging scenarios for AV testing efficiently. We validate our approach using Root Mean Square Error(RMSE) to compare the scenarios generated using the proposed parameters against the real-world trajectory data. In addition to that, we demonstrate that adding a slight disturbance to a few scenario parameters can generate different scenarios and utilise Responsibility-Sensitive Safety (RSS) metric to measure the scenarios' risk.
The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety of road transportation. Recent advances in V2X communication primarily address the definition of V2X messages and data dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a largely unsolved problem is how a connected vehicle (CV) can efficiently and consistently fuse its local perception information with the data received from other ITS-Ss. In this paper, we present a novel data fusion framework to fuse the local and V2X perception data for CP that considers the presence of cross-correlation. The proposed approach is validated through comprehensive results obtained from numerical simulation, CARLA simulation, and real-world experimentation that incorporates V2X-enabled intelligent platforms. The real-world experiment includes a CV, a connected and automated vehicle (CAV), and an intelligent roadside unit (IRSU) retrofitted with vision and lidar sensors. We also demonstrate how the fused CP information can improve the awareness of vulnerable road users (VRU) for CV/CAV, and how this information can be considered in path planning/decision making within the CAV to facilitate safe interactions.
Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite many safety challenges yet to be resolved. The modern autonomous vehicles will undoubtedly include machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. Road testing is essential before the deployment, but scenarios are repeatable, and it's hard to collect challenging events. Exploring numerous, diverse and crucial scenarios is a time-consuming and expensive approach. The research community and industry have widely accepted scenario-based testing in the last few years. As it is focused directly on the relevant critical road situations, it can reduce the effort required in testing. The scenario-based testing in simulation requires the realistic behaviour of the traffic participants to assess the System Under Test (SUT). It is essential to capture the scenarios from the real world to encode the behaviour of actual traffic participants. This paper proposes a novel scenario extraction method to capture the lane change scenarios using point-cloud data and object tracking information. This method enables fully automatic scenario extraction compared to similar approaches in this area. The generated scenarios are represented in OpenX format to reuse them in the SUT evaluation easily. The motivation of this framework is to build a validation dataset to generate many critical concrete scenarios. The code is available online at https://github.com/dkarunakaran/scenario_extraction_framework.
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brought about advances in mechanical, solid-state, and recently, adjustable scan pattern lidars. For the latter, existing works often require fine-tuning the model each time scan patterns are adjusted, which is infeasible. We explicitly deal with the sampling discrepancy by proposing a novel unsupervised multi-target domain adaptation framework, SEE, for transferring the performance of state-of-the-art 3D detectors across both fixed and flexible scan pattern lidars without requiring fine-tuning of models by end-users. Our approach interpolates the underlying geometry and normalizes the scan pattern of objects from different lidars before passing them to the detection network. We demonstrate the effectiveness of SEE on public datasets, achieving state-of-the-art results, and additionally provide quantitative results on a novel high-resolution lidar to prove the industry applications of our framework. This dataset and our code will be made publicly available.
Social acceptance is a major hurdle for autonomous vehicle technology, central to which is ensuring both passengers and nearby pedestrians feel safe. This idea of `feeling safe' and perceived safety is highly subjective and rooted in human intuition. As such, traditional analytical approaches to autonomous navigation often fail to cater for the social expectations of individuals. Therefore, this paper proposes an approach to capture the complexity of social expectations and integrate this complexity into a 3-layered Contextual Speed Controller. The layers were; the legal road speed limit, the socially acceptable speed given the number of nearby pedestrians, and the socially acceptable speed based on proximity to nearby pedestrians. An implementation of this layered approach was tested in areas of both low and high vehicle-pedestrian interactions. From the experiments conducted, the lower two layers were seen working in tandem to modulate the vehicle speed to appropriate levels that mimicked conservative human driver behaviour. In summary, this work quantified the relationship between pedestrian context and socially acceptable vehicle speeds, allowing for more perceivably safe autonomous driving. Furthermore, the need for different driving schemes for navigating different road environments was identified.
Robust and persistent localisation is essential for ensuring the safe operation of autonomous vehicles. When operating in large and diverse urban driving environments, autonomous vehicles are frequently exposed to situations that violate the assumptions of algorithms, suffer from the failure of one or more sensors, or other events that lead to a loss of localisation. This paper proposes the use of a behavior tree framework that can monitor the performance of localisation health metrics and triggers intelligent responses such as sensor switching and loss recovery. The algorithm presented selects the best available sensor data at given time and location, and can perform a series of actions to react to adverse situations. The behavior tree encapsulates the system-level logic to give commands that make up the intelligent behaviors, so that the localisation "actuators" (data association, optimisation, filters, etc) can perform decoupled actions without needing context. Experimental results to validate the algorithms are presented using the University of Sydney Campus dataset which was taken weekly over an 18 month period. A video showing the online localisation process can be found here: https://youtu.be/353uKqXLV5g
We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene. We minimise user error by automating the data selection process according to a metric, called Variability of Quality (VOQ) that gives a score to each calibration set of samples. We show that this VOQ score is correlated with the estimated calibration parameter's ability to generalise well to the entire scene, thereby overcoming the overfitting problems of existing calibration algorithms. Our approach has the benefits of simplifying the calibration process for practitioners of any calibration expertise level and providing an objective measure of the quality for our calibration pipeline's input and output data. We additionally use a novel method of assessing the accuracy of the calibration parameters. It involves computing reprojection errors for the entire scene to ensure that the parameters are well fitted to all features in the scene. Our proposed calibration pipeline takes 90s, and obtains an average reprojection error of 1-1.2cm, with standard deviation of 0.4-0.5cm over 46 poses evenly distributed in a scene. This process has been validated by experimentation on a high resolution, software definable lidar, Baraja Spectrum-Scan; and a low, fixed resolution lidar, Velodyne VLP-16. We have shown that despite the vast differences in lidar technologies, our proposed approach manages to estimate robust calibration parameters for both. Our code and data set used for this paper are made available as open-source.