Sensor scan timing compensation in environment models for automated road vehicles

Rieken, Jens; Maurer, Markus

doi:10.1109/itsc.2016.7795620

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…35 If the time step between two scans is sufficiently low (e.g. 0.1 s), 36 changes to the surrounding are already comprehensive of drivers’ intervention: 37 in this case, it is not necessary to foresee the scenario evolution making use of complex models for driver behaviour. 10…”

Section: Methodsmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable collision state. Currently available devices cannot properly handle an inevitable collision state, because its occurrence is not subject to evaluations by the system. In the present work, criteria for intervention on braking and steering are introduced, based on the vehicle occupants’ injury risk. The system must monitor the surrounding and act on the degrees of freedom adapting to the evolution of the scenario, following an adaptive logic. The model-in-the-loop, software-in-the-loop and hardware-in-the-loop for such adaptive intervention are first introduced. To highlight the potential benefits offered by the adaptive advanced driver assistance systems, simulation software has been developed. The adaptive logic has been tested in correspondence of three inevitable collision state conditions between two motor vehicles: at each instant, the adaptive logic attitude of creating impact configurations associated with minimum injury risk is ultimately demonstrated.

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Section: Methodsmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Unlike other approaches, which compensate for ego motion by transforming all measurements to a given point in time (e. g. [13], [20], [38], [39]), we propagate the time information through the preprocessing steps. In contrast to the references mentioned above, this allows a proper consideration of ego and target motion, as we have discussed in Rieken and Maurer [9].…”

Section: B Scan Definition and Timing Behaviormentioning

confidence: 99%

“…Based on previous results ( [3], [9]), the following section focuses on the integration of the aforementioned object model extensions. The results will be explained along the basic processing steps of recursive state estimation techniques.…”

Section: Object Trackingmentioning

confidence: 99%

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A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains

Rieken,

Maurer

2020

Preprint

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We present a LiDAR-based and real-time capable 3D perception system for automated driving in urban domains. The hierarchical system design is able to model stationary and movable parts of the environment simultaneously and under real-time conditions. Our approach extends the state of the art by innovative in-detail enhancements for perceiving road users and drivable corridors even in case of nonflat ground surfaces and overhanging or protruding elements. We describe a runtime-efficient pointcloud processing pipeline, consisting of adaptive ground surface estimation, 3D clustering and motion classification stages.Based on the pipeline's output, the stationary environment is represented in a multi-feature mapping and fusion approach. Movable elements are represented in an object tracking system capable of using multiple reference points to account for viewpoint changes. We further enhance the tracking system by explicit consideration of occlusion and ambiguity cases.Our system is evaluated using a subset of the TUBS Road User Dataset. We enhance common performance metrics by considering application-driven aspects of real-world traffic scenarios. The perception system shows impressive results and is able to cope with the addressed scenarios while still preserving real-time capability.

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“…Examples presented in Figure 1 illustrate misalignment of lidar and visual features in the environment when projecting uncorrected lidar points to images, which can cause degraded performance in the sensor fusion. Interested readers can refer to [2] for quantitative analyses of the time-related effects of moving scanning sensors on different perception tasks for multiple sensor systems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Egocentric Motion Correction of Lidar Point Cloud and Projection to Camera Images for Moving Platforms

Shan

Berrío

Nebot

2020

Preprint

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The fusion of sensor data from heterogeneous sensors is crucial for robust perception in various robotics applications that involve moving platforms, for instance, autonomous vehicle navigation. In particular, combining camera and lidar sensors enables the projection of precise range information of the surrounding environment onto visual images. It also makes it possible to label each lidar point with visual segmentation/classification results for 3D mapping, which facilitates a higher level understanding of the scene. The task is however considered non-trivial due to intrinsic and extrinsic sensor calibration, and the distortion of lidar points resulting from the ego-motion of the platform. Despite the existence of many lidar ego-motion correction methods, the errors in the correction process due to uncertainty in egomotion estimation are not possible to remove completely. It is thus essential to consider the problem a probabilistic process where the ego-motion estimation uncertainty is modelled and considered consistently. The paper investigates the probabilistic lidar ego-motion correction and lidar-to-camera projection, where both the uncertainty in the ego-motion estimation and time jitter in sensory measurements are incorporated. The proposed approach is validated both in simulation and using real-world data collected from an electric vehicle retrofitted with wide-angle cameras and a 16-beam scanning lidar.

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Sensor scan timing compensation in environment models for automated road vehicles

Cited by 10 publications

References 17 publications

Adaptive intervention logic for automated driving systems based on injury risk minimization

Adaptive intervention logic for automated driving systems based on injury risk minimization

A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains

Probabilistic Egocentric Motion Correction of Lidar Point Cloud and Projection to Camera Images for Moving Platforms

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