232 research outputs found
Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems
In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account
A survey on motion prediction and risk assessment for intelligent vehicles
International audienceWith the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model
Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving
Adverse weather conditions and occlusions in urban environments result in
impaired perception. The uncertainties are handled in different modules of an
automated vehicle, ranging from sensor level over situation prediction until
motion planning. This paper focuses on motion planning given an uncertain
environment model with occlusions. We present a method to remain collision free
for the worst-case evolution of the given scene. We define criteria that
measure the available margins to a collision while considering visibility and
interactions, and consequently integrate conditions that apply these criteria
into an optimization-based motion planner. We show the generality of our method
by validating it in several distinct urban scenarios
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
To safely and efficiently navigate through complex traffic scenarios,
autonomous vehicles need to have the ability to predict the future motion of
surrounding vehicles. Multiple interacting agents, the multi-modal nature of
driver behavior, and the inherent uncertainty involved in the task make motion
prediction of surrounding vehicles a challenging problem. In this paper, we
present an LSTM model for interaction aware motion prediction of surrounding
vehicles on freeways. Our model assigns confidence values to maneuvers being
performed by vehicles and outputs a multi-modal distribution over future motion
based on them. We compare our approach with the prior art for vehicle motion
prediction on the publicly available NGSIM US-101 and I-80 datasets. Our
results show an improvement in terms of RMS values of prediction error. We also
present an ablative analysis of the components of our proposed model and
analyze the predictions made by the model in complex traffic scenarios.Comment: accepted for publication at IV 201
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