Three Decades of Driver Assistance Systems: Review and Future Perspectives

By K. Bengler, Klaus C. J. Dietmayer, B. Färber, Markus Maurer, Christoph Stiller, Hermann Winner. IEEE Intelligent Transportation Systems Magazine, 2014

817 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/3ed2c33dd7d8289cd973a5fe4c9e7c9176bfffcb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Control and Systems Engineering

Citations in the corpus

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, methodology): “First introduced to automotive as navigation tools in driver assistance functions [13], currently GNSS is also used together with HD Maps for path planning and ego-vehicle localization for autonomous vehicles.”
Driver Behavior Analysis for Safe Driving: A Survey (2015, methodology): “Real-time analysis of driver behavior became quite popular research area with the emergence of ITS and VANET [119].”
Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions (2015, background): “For example, referencing a camera- with a laser scanner-based obstacle detection, see Fig.”
Autonomous Cars: Research Results, Issues, and Future Challenges (2019, background): “Another drawback with LIDAR is its high cost although has been decreasing recently and current LIDARs are more efficient [203].”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, neutral): “While radar has been used for decades [1, 3], we are not aware of any autonomous driving datasets that provide radar data.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, neutral): “[13] K.”
Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey (2018, background): “Therefore, it is essential the recognition of driving style (DS) and driver intention inference for the integration and development of these systems [1] – [7].”
Traffic state estimation on highway: A comprehensive survey (2017, background): “Traffic state estimation (TSE) refers to the process of inference of traffic state variables, namely flow (veh/h), density (veh/km), speed (km/h), and other equivalent variables, on road segments, using partially observed and noisy ∗Corresponding author Email addresses: t.seo@plan.cv.titech.ac.jp (T”
A Survey on Multimodal Large Language Models for Autonomous Driving (2023, background): “With such improvements, More L1 to L2 level Advanced Driver Assistance Systems (ADAS) like lane centering and adaptive cruise control are now available on everyday vehicles [11,21].”