16,630 research outputs found
Evaluation of estimation approaches on the quality and robustness of collision warning system
Vehicle safety is one of the most challenging aspect of future-generation
autonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as a
proposed solution framework, can be relied as the main structure to address the
issues in this area. In this framework, information plays a very important
role. Each vehicle has access to its own information immediately. However,
another vehicle information is available through a wireless communication. Data
loss is very common issue for such communication approach. As a consequence,
CCW would suffer from providing late or false detection awareness. Robust
estimation of lost data is of this paper interest which its goal is to
reconstruct or estimate lost network data from previous available or estimated
data as close to actual values as possible under different rate of lost. In
this paper, we will investigate and evaluate three different algorithms
including constant velocity, constant acceleration and Kalman estimator for
this purpose. We make a comparison between their performance which reveals the
ability of them in term of accuracy and robustness for estimation and
prediction based on previous samples which at the end affects the quality of
CCW in awareness generation
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Collective Perception: A Safety Perspective
Vehicle-to-everything (V2X) communication is seen as one of the main enabling technol-ogies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redun-dancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic par-ticipants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only con-tributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing world-wide standardization efforts of the collective perception service
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