Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety

By Mohan Manubhai Trivedi, Tarak Gandhi, Joel C. McCall. IEEE transactions on intelligent transportation systems (Print), 2007

341 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Social Psychology

View Paper: https://www.semanticscholar.org/paper/609e3dde576e5bc9ea382d66512dd0864c4f8529

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

In Automotive Engineering

In Computer Vision and Pattern Recognition

In Social Psychology

Citations in the corpus

Examining the impact of driving style on the predictability and responsiveness of the driver: Real-world and simulator analysis (2010, background): “Researchers have recently developed novel approaches to advanced, intelligent Driver Assistance Systems incorporating sensor data from the driver, vehicle, and surround [1], [2].”
Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness (2010, background): “Recently, there has been a great interest in driver-assistance systems that use computer vision technology to develop safer automobiles [3].”
Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey (2012, background): “As described in [1]–[3], it is crucial to not only consider the car’s surrounding and external environment when designing an assist system but also consider the internal environment and take the driver into account.”
Three Decades of Driver Assistance Systems: Review and Future Perspectives (2014, background): “Interior cameras can recognize this state and adapt the parameters of warning and intervention systems (Trivedi et al. 2007).”
A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking (2010, methodology): “It is widely recognized that computer vision is a critical technology for the development of intelligent vehicles [32].”
Pedestrian Protection Systems: Issues, Survey, and Challenges (2007, background): “For this purpose, it is important to not only look outside the vehicle to detect dangerous situations but also look inside the vehicle in order to assess the state and intent of the driver [ 96 ].”