2023 IEEE Intelligent Vehicles Symposium (IV)最新文献

{"title":"Example-Based Query To Identify Causes of Driving Anomaly with Few Labeled Samples","authors":"Yuning Qiu, Teruhisa Misu, C. Busso","doi":"10.1109/IV55152.2023.10186733","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186733","url":null,"abstract":"Driving anomaly detection is important for advanced driver assistance systems (ADAS) to increase driving safety and avoid traffic accidents. However, driving anomaly detection faces many challenges such as numerous and uncertain abnormal patterns observed on the road, sparsity of real anomaly cases documented with accurate labels, and rigid existing systems that rely on manually set thresholds and rules. Previous studies have proposed unsupervised methods for driving anomaly detection in the driver’s behaviors or the road condition by identifying deviations from normal driving conditions. A challenge with unsupervised models is the lack of interpretability, where the cause of the anomaly is not always clear. We address this problem with an example-based query method that combines unsupervised anomaly detection methods with the multi-label k-nearest neighbors (ML-KNN) algorithm to interpret the detected driving anomalies by identifying their possible causes (e.g., surrounding objects or driver’s errors). Our approach relies on a few manually labeled driving segments that are efficiently used as anchors to retrieve the causes of driving anomalies in a given driving segment. These anchors are projected into the embedding created by unsupervised driving anomaly detection systems. The experimental results show that this method can effectively identify the causes of driving anomalies, even for abnormal driving segments triggered by multiple causes. The evaluation shows the flexibility of our proposed solution, where we successfully implement the ML-KNN approach with three alternative feature representations.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115369981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bare-Metal vs. Hypervisors and Containers: Performance Evaluation of Virtualization Technologies for Software-Defined Vehicles","authors":"Long Wen, Markus Rickert, F. Pan, Jianjie Lin, A. Knoll","doi":"10.1109/IV55152.2023.10186789","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186789","url":null,"abstract":"Software-defined vehicles (SDV) play an important role in future electrical and electronic (E&E) architectures. Their increased flexibility compared to traditional architectures is a crucial factor in the rapid development cycles of autonomous driving. Containerization and virtualization are two key technologies that enable rapid software installation and updates under the SDV framework. These two technologies have been widely adopted in cloud computing, but their performance and suitability in intelligent vehicles still has to be evaluated. In this work, we look at generic performance experiments of containerization and virtualization on both embedded and general-purpose computer systems regarding CPU, memory, network, and disk. We further investigate the impact of virtualization and containerization on the Autoware framework to evaluate scenarios that are close to real-world automotive applications. Additionally, we evaluate performance by splitting the Autoware framework into several dependent service parts, which are installed in separate containers. Extensive experimental results show that virtualization and containerization have no significant performance drop with 0-5% loss compared to a bare-metal setup in terms of CPU, memory, and network. However, both technologies suffer dramatic performance degradation on the disk side, losing 5-15% in containers and 35% in virtualization.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125066243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Decision Making for Autonomous Driving in Complex Urban Scenarios","authors":"Rodrigo Gutiérrez-Moreno, R. Barea, M. E. L. Guillén, J. F. Arango, Navil Abdeselam, L. Bergasa","doi":"10.1109/IV55152.2023.10186666","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186666","url":null,"abstract":"Autonomous driving presents significant challenges due to the variability of behaviours exhibited by surrounding vehicles and the diversity of scenarios encountered. To address these challenges, we propose a hybrid architecture that combines traditional and deep learning techniques. Our architecture includes strategy, tactical and execution modules. Specifically, the strategy module defines the trajectory to be followed. Then, the tactical decision module employs a proximal policy optimization algorithm and deep reinforcement learning. Finally, the maneuver execution module uses a linear-quadratic regulator controller for trajectory tracking and a predictive model controller for lane change execution. This hybrid architecture and the comparison with other classical approaches are the main contributions of this research. Experimental results demonstrate that the proposed framework solves concatenated complex urban scenarios optimally.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129864303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-to-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles","authors":"Pengfei Lin, E. Javanmardi, Ye Tao, Vishal Chauhan, Jin Nakazato, Manabu Tsukada","doi":"10.1109/IV55152.2023.10186619","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186619","url":null,"abstract":"Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121407866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Supervised Occupancy Grid Map Completion for Automated Driving","authors":"Jugoslav Stojcheski, Thomas Nürnberg, Michael Ulrich, T. Michalke, Claudius Gläser, Andreas Geiger","doi":"10.1109/IV55152.2023.10186748","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186748","url":null,"abstract":"This paper investigates methods for enhancing the quality of occupancy grid maps (OGMs) using a combination of a self-supervised data generation procedure using only unlabeled data and a deep learning approach. OGMs are grid-structured environment representations, commonly used in automated driving systems to encode occupancy of the surrounding area. However, due to limited sensor range and resolution, their quality degrades significantly in distant and occluded areas, posing a challenge for a subsequent decision making. We introduce OGM completion, whose goal is to provide a more complete representation of the environment by extrapolating potential occupancy to distant and occluded areas. In particular, we propose and implement a complete framework for OGM completion. We develop a method for self-supervised data generation, identify an existing class of adoptable deep learning architectures, adapt loss functions and a quantitative performance metric, and derive a generic baseline method. Finally, we validate the functionality of the implemented framework by thorough experimentation and inspection of real-world examples of OGM completion in automated driving, significantly outperforming a baseline method.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116133275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset","authors":"Jeldrik Axmann, Rozhin Moftizadeh, Jing-wen Su, B. Tennstedt, Qianqian Zou, Yunshuang Yuan, Dominik Ernst, H. Alkhatib, C. Brenner, S. Schön","doi":"10.1109/IV55152.2023.10186693","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186693","url":null,"abstract":"Recently published datasets have been increasingly comprehensive with respect to their variety of simultaneously used sensors, traffic scenarios, environmental conditions, and provided annotations. However, these datasets typically only consider data collected by one independent vehicle. Hence, there is currently a lack of comprehensive, real-world, multi-vehicle datasets fostering research on cooperative applications such as object detection, urban navigation, or multi-agent SLAM. In this paper, we aim to fill this gap by introducing the novel LUCOOP dataset, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles. The driving scenario corresponds to a follow-up setup of multiple rounds in an inner city triangular trajectory. Each vehicle was equipped with a broad sensor suite including at least one LiDAR sensor, one GNSS antenna, and up to three IMUs. Additionally, Ultra-Wide-Band (UWB) sensors were mounted on each vehicle, as well as statically placed along the trajectory enabling both V2V and V2X range measurements. Furthermore, a part of the trajectory was monitored by a total station resulting in a highly accurate reference trajectory. The LUCOOP dataset also includes a precise, dense 3D map point cloud, acquired simultaneously by a mobile mapping system, as well as an LOD2 city model of the measurement area. We provide sensor measurements in a multi-vehicle setup for a trajectory of more than 4 km and a time interval of more than 26 minutes, respectively. Overall, our dataset includes more than 54,000 LiDAR frames, approximately 700,000 IMU measurements, and more than 2.5 hours of 10 Hz GNSS raw measurements along with 1 Hz data from a reference station. Furthermore, we provide more than 6,000 total station measurements over a trajectory of more than 1 km and 1,874 V2V and 267 V2X UWB measurements. Additionally, we offer 3D bounding box annotations for evaluating object detection approaches, as well as highly accurate ground truth poses for each vehicle throughout the measurement campaign.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116358492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the Crash Risk of Vehicle-Pedestrian Interactions using Autonomous Vehicle Data","authors":"Gabriel Lanzaro, Chuanyun Fu, T. Sayed","doi":"10.1109/IV55152.2023.10186754","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186754","url":null,"abstract":"There is an increasing interest in autonomous vehicles (AVs) research as they are expected to provide considerable safety and mobility benefits. These vehicles should be able to interact with road users safely, which requires understanding the behavior of actual interactions between human-driven vehicles (HDV) and vulnerable road users (e.g., pedestrians). However, such behavior may vary considerably depending on the driving environment as culture plays an important role in traffic safety. This study uses an Extreme Value Theory Peak Over Threshold framework to estimate the risk of vehicle-pedestrian interactions in four different cities in the US and Asia (i.e., Boston, Las Vegas, Pittsburgh, and Singapore). A Bayesian hierarchical structure is considered to incorporate the effect of different covariates, which enables estimating the risk for each interaction. A large-scale AV dataset is used. As AVs are equipped with several sensors, they can capture information about the environment in real-time, including other road users’ positions and speeds. Results show that the risk varies significantly across different cities. For example, Pittsburgh has a greater risk than Singapore for regular vehicle-pedestrian interactions, which indicates that some cities require additional efforts for the implementation of AVs as the risk of interactions with pedestrians varies. Therefore, modeling frameworks that account for site-specific behavioral parameters should be proposed for the safe coexistence between advanced technologies and vulnerable road users.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121691868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Lane-changing Behavior Planning via Reinforcement Learning with Imitation Learning Initialization","authors":"Jiamin Shi, Tangyike Zhang, Junxiang Zhan, Shi-tao Chen, J. Xin, Nanning Zheng","doi":"10.1109/IV55152.2023.10186577","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186577","url":null,"abstract":"Robust lane-changing behavior planning is critical to ensuring the safety and comfort of autonomous vehicles. In this paper, we proposed an efficient and robust vehicle lane-changing behavior decision-making method based on reinforcement learning (RL) and imitation learning (IL) initialization which learns the potential lane-changing driving mechanisms from driving mechanism from the interactions between vehicle and environment, so as to simplify the manual driving modeling and have good adaptability to the dynamic changes of lane-changing scene. Our method further makes the following improvements on the basis of the Proximal Policy Optimization (PPO) algorithm: (1) A dynamic hybrid reward mechanism for lane-changing tasks is adopted; (2) A state space construction method based on fuzzy logic and deformation pose is presented to enable behavior planning to learn more refined tactical decision-making; (3) An RL initialization method based on imitation learning which only requires a small amount of scene data is introduced to solve the low efficiency of RL learning under sparse reward. Experiments on the SUMO show the effectiveness of the proposed method, and the test on the CARLA simulator also verifies the generalization ability of the method.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122955379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"I Had a Bad Day: Challenges of Object Detection in Bad Visibility Conditions","authors":"Thomas Rothmeier, Diogo Wachtel, Tetmar von Dem Bussche-Hünnefeld, W. Huber","doi":"10.1109/IV55152.2023.10186674","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186674","url":null,"abstract":"Automated vehicles must be able to correctly perceive the environment in every conceivable situation with the help of their sensor stack. The resulting data streams are typically processed by algorithms that detect and classify objects in the scene. In order to ensure safe driving, these algorithms are expected to perform with sufficient accuracy even in the harshest weather conditions, such as rain, fog and snow. However, this is not the case for the current generation of object detection models. A sharp drop in performance can be observed as soon as these are exposed to adverse weather situations. This can be attributed to the weak representation of training data in bad visibility conditions and detection architectures that are not designed to handle them properly. To address this problem, we propose three small scale annotated datasets that include challenging adverse weather conditions. We evaluate state-of-the art object detection models on a variety of datasets and quantify the performance drop for each algorithm in adverse weather conditions. To this end, we show that current object detection models suffer from severe performance loss due to adverse weather effects and identify common challenges of object detection in bad visibility conditions.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115952162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corner Cases in Data-Driven Automated Driving: Definitions, Properties and Solutions","authors":"Jingxing Zhou, Jürgen Beyerer","doi":"10.1109/IV55152.2023.10186558","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186558","url":null,"abstract":"The field of validation and artificial intelligence (AI) for automated driving has been a rapidly emerging field of research and development in the last few years. Despite the enormous success of machine learning (ML) in perception and robotics, the capability of ML-supported automated driving functions remains to be proven in complex real-world scenarios. Due to stringent regulations and safety concerns, it is crucial to not only be able to identify critical driving events, the corner cases, but also to eliminate them in advance by systematic and provable processes. In contrast to previous work, we analyze and systematize the causes of corner cases from the perspective of neural network interpretation, and consider the network’s performance and robustness in relation to the availability of data points used during development and validation. Moreover, we demonstrate the proposed taxonomy of corner cases on real data from multiple sensor input sources, including images and LiDAR point clouds, showing relevant properties of various corner cases. Furthermore, we discuss the possible solutions dealing with previously unknown classes and driving environments as required in future automated driving use cases.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125725926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}