#lanechangeprediction Suchergebnisse
Neues probabilistisches Prädiktionsmodell für Spurwechsel auf Autobahnen vorgestellt. #GausianProcessNeuralNetwork #gpnn #LaneChangePrediction #IEEE #ITSC #ITSS #Auckland #Kiwi
The imbalance issue in #LaneChangePrediction (much #LaneKeeping and few #LaneChange s) can also be fight by adapting the threshold for discretizing the softmax output into categorical decisions. Many of those techniques are based on the #ROCCurve.
Predicting surrounding vehicles motion is crucial for #automateddriving. RST has already developed algorithms for #lanechangeprediction and #trajectoryprediction. Now we work on the hardest problem interaction-aware, multi-agent scene prediction. #selfdrivingcars #deeplearning
At #VDI #Mechatronik Tagung we presented our results on strategies to improve the #LaneChangePrediction accuracy by combining real-world & simulation data. Since it’s a crucial function for #AutomatedDriving we investigated methods for efficiently selecting training data too.
#DTS #AutonomousVehicles #LaneChangePrediction Predicting lane changes in autonomous vehicles is crucial for safety. Using AI and sensing tech, this study proposes a framework for accurate predictions. Details: maxapress.com/article/doi/10…
In highway traffic #LaneChange s are rare. For #LaneChangePrediction this is complicating the training of a #MachineLearning based model and evaluating it afterwards. See ieeexplore.ieee.org/document/84028… for how we worked on this issue with different sampling methods.
#LaneChangePrediction is important for #AutomatedDriving in highway situations. To bring #AI-based methods such as #NeuralNetworks to ECUs computational efficiency, which we investigated publikationen.bibliothek.kit.edu/1000061936/393…, is important too.
In highway driving #LaneChangePrediction of vehicles constitutes a valuable information of their future motion. Beside other vehicles causing or hindering a #LaneChange, further factors influence driver behavior. See link.springer.com/chapter/10.100… for more information.
For highway targeted #ADAS in mass market applications #LaneChangePrediction represents a valuable information. We developed a #NeuralNetworks based classifier & analyzed the dependence on labeling methods. sciencedirect.com/science/articl…
#DTS #AutonomousVehicles #LaneChangePrediction Predicting lane changes in autonomous vehicles is crucial for safety. Using AI and sensing tech, this study proposes a framework for accurate predictions. Details: maxapress.com/article/doi/10…
At #VDI #Mechatronik Tagung we presented our results on strategies to improve the #LaneChangePrediction accuracy by combining real-world & simulation data. Since it’s a crucial function for #AutomatedDriving we investigated methods for efficiently selecting training data too.
#LaneChangePrediction is important for #AutomatedDriving in highway situations. To bring #AI-based methods such as #NeuralNetworks to ECUs computational efficiency, which we investigated publikationen.bibliothek.kit.edu/1000061936/393…, is important too.
The imbalance issue in #LaneChangePrediction (much #LaneKeeping and few #LaneChange s) can also be fight by adapting the threshold for discretizing the softmax output into categorical decisions. Many of those techniques are based on the #ROCCurve.
In highway traffic #LaneChange s are rare. For #LaneChangePrediction this is complicating the training of a #MachineLearning based model and evaluating it afterwards. See ieeexplore.ieee.org/document/84028… for how we worked on this issue with different sampling methods.
In highway driving #LaneChangePrediction of vehicles constitutes a valuable information of their future motion. Beside other vehicles causing or hindering a #LaneChange, further factors influence driver behavior. See link.springer.com/chapter/10.100… for more information.
For highway targeted #ADAS in mass market applications #LaneChangePrediction represents a valuable information. We developed a #NeuralNetworks based classifier & analyzed the dependence on labeling methods. sciencedirect.com/science/articl…
Predicting surrounding vehicles motion is crucial for #automateddriving. RST has already developed algorithms for #lanechangeprediction and #trajectoryprediction. Now we work on the hardest problem interaction-aware, multi-agent scene prediction. #selfdrivingcars #deeplearning
Neues probabilistisches Prädiktionsmodell für Spurwechsel auf Autobahnen vorgestellt. #GausianProcessNeuralNetwork #gpnn #LaneChangePrediction #IEEE #ITSC #ITSS #Auckland #Kiwi
Neues probabilistisches Prädiktionsmodell für Spurwechsel auf Autobahnen vorgestellt. #GausianProcessNeuralNetwork #gpnn #LaneChangePrediction #IEEE #ITSC #ITSS #Auckland #Kiwi
The imbalance issue in #LaneChangePrediction (much #LaneKeeping and few #LaneChange s) can also be fight by adapting the threshold for discretizing the softmax output into categorical decisions. Many of those techniques are based on the #ROCCurve.
#DTS #AutonomousVehicles #LaneChangePrediction Predicting lane changes in autonomous vehicles is crucial for safety. Using AI and sensing tech, this study proposes a framework for accurate predictions. Details: maxapress.com/article/doi/10…
Predicting surrounding vehicles motion is crucial for #automateddriving. RST has already developed algorithms for #lanechangeprediction and #trajectoryprediction. Now we work on the hardest problem interaction-aware, multi-agent scene prediction. #selfdrivingcars #deeplearning
At #VDI #Mechatronik Tagung we presented our results on strategies to improve the #LaneChangePrediction accuracy by combining real-world & simulation data. Since it’s a crucial function for #AutomatedDriving we investigated methods for efficiently selecting training data too.
#LaneChangePrediction is important for #AutomatedDriving in highway situations. To bring #AI-based methods such as #NeuralNetworks to ECUs computational efficiency, which we investigated publikationen.bibliothek.kit.edu/1000061936/393…, is important too.
In highway traffic #LaneChange s are rare. For #LaneChangePrediction this is complicating the training of a #MachineLearning based model and evaluating it afterwards. See ieeexplore.ieee.org/document/84028… for how we worked on this issue with different sampling methods.
In highway driving #LaneChangePrediction of vehicles constitutes a valuable information of their future motion. Beside other vehicles causing or hindering a #LaneChange, further factors influence driver behavior. See link.springer.com/chapter/10.100… for more information.
For highway targeted #ADAS in mass market applications #LaneChangePrediction represents a valuable information. We developed a #NeuralNetworks based classifier & analyzed the dependence on labeling methods. sciencedirect.com/science/articl…
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