Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles
By Joel C. McCall, Mohan Manubhai Trivedi. 2007
231 citations recorded by the source corpus.
Subfields: Automotive Engineering, Safety, Risk, Reliability and Quality, Social Psychology
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
- Jacob Lambert
- Alexander Carballo
- Ekim Yurtsever
- Derick A. Johnson
- Kazuya Takeda
- Mohan Manubhai Trivedi
- Anup Doshi
In Safety, Risk, Reliability and Quality
- Derick A. Johnson
- Mohan Manubhai Trivedi
- Kazuya Takeda
- Alexander Carballo
- Ekim Yurtsever
- Jacob Lambert
In Social Psychology
- Derick A. Johnson
- Mohan Manubhai Trivedi
- Alexander Carballo
- Ekim Yurtsever
- Kazuya Takeda
- Jacob Lambert
- Anup Doshi
Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Bayesian approaches were utilized in [236] for modeling driving style on roundabouts and in [237] to asses critical braking situations.”
- Driving style recognition using a smartphone as a sensor platform (2011, methodology): “Hidden Markov Models (HMM) were trained and used only on the acceleration data for recognition of simple driving patterns.”
- Examining the impact of driving style on the predictability and responsiveness of the driver: Real-world and simulator analysis (2010, background): “This “holistic” approach has been successful in creating active systems to predict driver behaviors in a number of maneuvers [3], [4], [5], [6].”
- Multi-Factor Authentication: A Survey (2018, background): “…to monitor the driver-specific features [129,130], which could be analyzed from two perspectives: (i) vehicle-specific behavior: steering angle sensor, speed sensor, brake pressure sensor, etc. [131,132]; and (ii) human factors: music played, calls made, presence of people in the car, etc. [133].”
- Classification of Highway Lane Change Behavior to Detect Dangerous Cut-in Maneuvers (2015, background): “Especially, in situations where an overtaking vehicle needs to avoid imminent crash in the original lane, the driver will have little time to check the target lane and to maintain a normal lane change trajectory ( 7, 8 ).”
- Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning (2017, unknown): “, vehicle assistance control [1]–[3].”