6 research outputs found
Radar-based Dynamic Occupancy Grid Mapping and Object Detection
Environment modeling utilizing sensor data fusion and object tracking is
crucial for safe automated driving. In recent years, the classical occupancy
grid map approach, which assumes a static environment, has been extended to
dynamic occupancy grid maps, which maintain the possibility of a low-level data
fusion while also estimating the position and velocity distribution of the
dynamic local environment. This paper presents the further development of a
previous approach. To the best of the author's knowledge, there is no
publication about dynamic occupancy grid mapping with subsequent analysis based
only on radar data. Therefore in this work, the data of multiple radar sensors
are fused, and a grid-based object tracking and mapping method is applied.
Subsequently, the clustering of dynamic areas provides high-level object
information. For comparison, also a lidar-based method is developed. The
approach is evaluated qualitatively and quantitatively with real-world data
from a moving vehicle in urban environments. The evaluation illustrates the
advantages of the radar-based dynamic occupancy grid map, considering different
comparison metrics.Comment: Accepted to be published as part of the 23rd IEEE International
Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece,
September 20-23, 202
Exploiting Posit Arithmetic for Deep Neural Networks in Autonomous Driving Applications
This paper discusses the introduction of an integrated Posit Processing Unit (PPU) as an alternative to Floating-point Processing Unit (FPU) for Deep Neural Networks (DNNs) in automotive applications. Autonomous Driving tasks are increasingly depending on DNNs. For example, the detection of obstacles by means of object classification needs to be performed in real-time without involving remote computing. To speed up the inference phase of DNNs the CPUs on-board the vehicle should be equipped with co-processors, such as GPUs, which embed specific optimization for DNN tasks. In this work, we review an alternative arithmetic that could be used within the co-processor. We argue that a new representation for floating point numbers called Posit is particularly advantageous, allowing for a better trade-off between computation accuracy and implementation complexity. We conclude that implementing a PPU within the co-processor is a promising way to speed up the DNN inference phase
Improved dynamic object detection within evidential grids framework
International audienceThe deployment of autonomous robots/vehicles is increasing in several domains. To perform tasks properly, a robot must have a good perception about its environment while detecting dynamic obstacles. Recently, evidential grids have attracted more interest for environment perception since they permit more effective uncertainty handling. The latest studies on evidential grids relied on the use of thresholds for information management e.g. the use of a threshold, for the conflict characterized by the mass of empty set, in order to detect dynamic objects. Nevertheless, the mass of empty set alone is not consistent in some cases. Also, the thresholds used were chosen either arbitrary or tuned manually without any computational method. In this paper, first the conflict is composed of two parameters instead of mass of empty set alone, and dynamic objects detection is performed using a threshold on the evolution of this conflict pair. Secondly, the paper introduces a general threshold along with a mathematical demonstration to compute it which can be used in different dynamic object detection cases. A real-time experiment is performed using the RB1-BASE robot equipped with a RGB-D camera and a laser scanner
Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps
Autonomously driving vehicles require a complete and robust perception of the
local environment. A main challenge is to perceive any other road users, where
multi-object tracking or occupancy grid maps are commonly used. The presented
approach combines both methods to compensate false positives and receive a
complementary environment perception. Therefore, an environment perception
framework is introduced that defines a common representation, extracts objects
from a dynamic occupancy grid map and fuses them with tracks of a Labeled
Multi-Bernoulli filter. Finally, a confidence value is developed, that
validates object estimates using different constraints regarding physical
possibilities, method specific characteristics and contextual information from
a digital map. Experimental results with real world data highlight the
robustness and significance of the presented fusing approach, utilizing the
confidence value in rural and urban scenarios