SmartMOT: Exploiting the fusion of HDMaps and Multi-Object Tracking for Real-Time scene understanding in Intelligent Vehicles applications

Gómez-Huélamo, Carlos; Bergasa, Luis M.; Gutiérrez, Raquel Lázaro; Arango, J. Felipe; Díaz, Aníbal Nieto

doi:10.1109/iv48863.2021.9575443

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…[17] Webots [76], [136] CARLA (Unreal Engine) [3], [21], [52], [60], [62], [196] CARLA (Unreal Engine) + Autoware…”

Section: Simulator Typementioning

confidence: 99%

“…Overcoming the challenges associated with implementing and testing robots that integrate AI techniques, including ensuring safety, managing the cost of testing scenarios, improving training speed, and addressing scalability issues, is crucial for researchers [1]- [3]. By addressing these challenges through simulation, researchers can ensure the reliability and effectiveness of the robots they design.…”

Section: Introductionmentioning

confidence: 99%

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Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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“…[17] Webots [76], [136] CARLA (Unreal Engine) [3], [21], [52], [60], [62], [196] CARLA (Unreal Engine) + Autoware…”

Section: Simulator Typementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…Traditional methods for motion forecasting [1], [10] are based on physical kinematic constraints and road map information with handcrafted rules. Though these approaches are sufficient in many simple situations (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…is a research topic by itself [16], [24], [25] and a core part in the AD pipeline. In that sense, researchers have identified a bottleneck for efficient real-time applications [1], [10], as usually, more data-inputs implies higher complexity and inference time [26]. As observed, DL methods are data-hungry and use complex features (both social and physical) to predict, in an accurate way, the future behaviour of the agents in the scene.…”

Section: Introductionmentioning

confidence: 99%

Exploring Attention GAN for Vehicle Motion Prediction

Gómez-Huélamo¹,

Conde²,

Ortiz³

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Motion Prediction (MP) of multiple surroundings agents is a crucial task in arbitrarily complex environments, from simple robots to Autonomous Driving Stacks (ADS). Current techniques tackle this problem using end-to-end pipelines, where the input data is usually a rendered top-view of the physical information and the past trajectories of the most relevant agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable ADS must produce reasonable predictions on time. However, despite many approaches use simple ConvNets and LSTMs to obtain the social latent features, State-Of-The-Art (SOTA) models might be too complex for realtime applications when using both sources of information (map and past trajectories) as well as little interpretable, specially considering the physical information. Moreover, the performance of such models highly depends on the number of available inputs for each particular traffic scenario, which are expensive to obtain, particularly, annotated High-Definition (HD) maps.In this work, we propose several efficient baselines for the well-known Argoverse 1 Motion Forecasting Benchmark. We aim to develop compact models using SOTA techniques for MP, including attention mechanisms and GNNs. Our lightweight models use standard social information and interpretable map information such as points from the driveable area and plausible centerlines by means of a novel preprocessing step based on kinematic constraints, in opposition to black-box CNN-based or toocomplex graphs methods for map encoding, to generate plausible multimodal trajectories achieving up-to-pair accuracy with less operations and parameters than other SOTA methods. Our code is publicly available at https://github.com/Cram3r95/mapfe4mp.

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“…Traditional methods for motion forecasting [1], [15], [16] are based on physical kinematic constraints and road map information with handcrafted rules. Though these approaches are sufficient in many simple situations (i.e.…”

Section: Introductionmentioning

confidence: 99%

Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction

Gómez-Huélamo¹,

Conde²,

Ortiz³

2022

Preprint

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Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex environments, from social robots to self-driving cars. Current approaches tackle this problem using end-to-end networks, where the input data is usually a rendered top-view of the scene and the past trajectories of all the agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable Autonomous Driving (AD) system must produce reasonable predictions on time, however, despite many of these approaches use simple ConvNets and LSTMs, models might not be efficient enough for real-time applications when using both sources of information (map and trajectory history). Moreover, the performance of these models highly depends on the amount of training data, which can be expensive (particularly the annotated HD maps). In this work, we explore how to achieve competitive performance on the Argoverse 1.0 Benchmark using efficient attention-based models, which take as input the past trajectories and map-based features from minimal map information to ensure efficient and reliable MP. These features represent interpretable information as the driveable area and plausible goal points, in opposition to black-box CNN-based methods for map processing. Our code is publicly available at https://github.com/Cram3r95/mapfe4mp.

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SmartMOT: Exploiting the fusion of HDMaps and Multi-Object Tracking for Real-Time scene understanding in Intelligent Vehicles applications

Cited by 9 publications

References 10 publications

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Exploring Attention GAN for Vehicle Motion Prediction

Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction

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