Toward Perception-Driven Urban Environment Modeling for Automated Road Vehicles

Rieken, Jens; Matthaei, Richard; Maurer, Markus

doi:10.1109/itsc.2015.124

Cited by 18 publications

(17 citation statements)

References 16 publications

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“…In the given example, we modeled epistemic uncertainty caused by occluded space in an aleatoric distribution and show how formulating soft constraints can be used to differentiate between a conservative and optimistic driving styles. For future work, the proposed system is currently being integrated in our research vehicles MOBILE [23] and Leonie and will be validated using the environment perception framework described in [16].…”

Section: Discussionmentioning

confidence: 99%

“…as measurement noise in inverse sensor models for occupancy grids or model-based filtering algorithms for vehicle tracking, approaches which explicitly represent epistemic uncertainty (e.g. [11], [16], [3]) are not as frequently applied in the field of automated driving. For this paper, we assume a grid-based environment representation as presented in [16], combining multiple grid layers such that drivable but occluded spaces can be identified as well as lane markings, curbs and higher elevated objects in the surroundings.…”

Section: Challenges In Motion Planningmentioning

confidence: 99%

“…[11], [16], [3]) are not as frequently applied in the field of automated driving. For this paper, we assume a grid-based environment representation as presented in [16], combining multiple grid layers such that drivable but occluded spaces can be identified as well as lane markings, curbs and higher elevated objects in the surroundings. The fine grained semantic differentiation between elements in the grid representation is already an approach to represent and decrease epistemic uncertainty.…”

Section: Challenges In Motion Planningmentioning

confidence: 99%

See 2 more Smart Citations

Representing the Unknown – Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation

Nolte

Ernst

Richelmann

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Even though motion planning for automated vehicles has been extensively discussed for more than two decades, it is still a highly active field of research with a variety of different approaches having been published in the recent years. When considering the market introduction of SAE Level 3+ vehicles, the topic of motion planning will most likely be subject to even more detailed discussions between safety and user acceptance. This paper shall discuss parameters of the motion planning problem and requirements to an environment model. The focus is put on the representation of different types of uncertainty at the example of sensor occlusion, arguing the importance of a well-defined interface between decision making and trajectory generation.

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Section: Discussionmentioning

confidence: 99%

Section: Challenges In Motion Planningmentioning

confidence: 99%

Section: Challenges In Motion Planningmentioning

confidence: 99%

See 1 more Smart Citation

Representing the Unknown – Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation

Nolte

Ernst

Richelmann

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Self Cite

View full text Add to dashboard Cite

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“…Other works have also considered learning based neural network models to estimate the object pose [38], by training the detectors from all possible view angles. In this work, minimum rectangle area [39] with L-shape cloud fitting [9] is utilized as in [13,40], with optimized computational and accuracy considerations.…”

Section: Object Detectionmentioning

confidence: 99%

Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Sualeh

Kim

2019

Sensors

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Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.

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“…While the environment perception system is mainly based on LiDAR scanners (based on [14]), a radar sensor and a camera will be added in the future to provide additional information be used to monitor the environment around the car. Data from the LiDAR sensors is used to create a map of the static environment, which provides the basis for modelbased trajectory planning.…”

Section: System and Threat Modelmentioning

confidence: 99%

A framework for policy based secure intra vehicle communication

Hamad

Nolte

Prevelakis

2017

2017 IEEE Vehicular Networking Conference (VNC)

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Over the past two decades, significant developments were introduced within the vehicular domain, evolving the modern vehicle into a network of dozens of embedded systems each hosting one or more applications. Communications within this distributed environment while adhering to safety-critical and secure systems guidelines implies the formulation of a comprehensive and consistent communications policy. Creating this policy is a complex, error-prone and labor-intensive task, requiring detailed knowledge of possible communication paths between all possible components of the system. For this reason, it is often skipped, trusting that each task will behave as intended and interact only with its peers. Traditional testing provides sufficient confidence to allow certification. Nevertheless, the existing process ignores malicious interference, whereby an adversary compromises a low-criticality process or subsystem and uses that to attack other subsystems, effectively taking over the vehicle. In this paper, we propose a framework to build a secure communications policy gradually by integrating it through the design and life cycle of vehicle's software components. We also propose a security module which acts as a connection policy checker vetting the incoming and outgoing communications and enforcing the distributed security policy.

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Toward Perception-Driven Urban Environment Modeling for Automated Road Vehicles

Cited by 18 publications

References 16 publications

Representing the Unknown – Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation

Representing the Unknown – Impact of Uncertainty on the Interaction between Decision Making and Trajectory Generation

Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

A framework for policy based secure intra vehicle communication

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