Robust multi-model longitudinal tire-force estimation scheme: Experimental data validation

Vazquez, Angel Gabriel Alatorre; Victorino, Alessandro Corrêa; Charara, Ali

doi:10.1109/itsc.2017.8317799

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beside OEMs, various research groups are working on novel technologies which provide enhancements in safety and efficiency of passenger vehicles. On one side, we have advanced vehicle state estimation [2][3][4], followed by safe and agile maneuvering in extreme conditions [5], and improved control of steering feedback to the driver [6]. On the other side, improved safety is considered in several areas, starting from a shared control system where the human and ADFs are working in synergy [7], to research focusing on pedestrian safety [8], and in the development of future human-machine interfaces that should reduce driver distraction [9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems

et al. 2018

View full text Add to dashboard Cite

Test, verification, and development activities of vehicles with ADAS (Advanced Driver Assistance Systems) and ADF (Automated Driving Functions) generate large amounts of measurement data. To efficiently evaluate and use this data, a generic understanding and classification of the relevant driving scenarios is necessary. Currently, such understanding is obtained by using heuristic algorithms or even by manual inspection of sensor signals. In this paper, we apply deep learning on sensor time series data to automatically extract relevant features for classification of driving scenarios relevant for a Lane-Keep-Assist System. We compare the performance of convolutional and recurrent neural networks and propose two classification models. The first one is an online model for scenario classification during driving. The second one is an offline model for post-processing, providing higher accuracy.

show abstract