Cognitive Models for Intelligent Driving Assistance

Zhihao Jiang, Renzhi Tang, Junyu Diao, Yi Gu
Apr 27, 2021
Cognitive Models of Drivers

Human driver cannot be completely replaced by AI in the forseeable future, which means future ADAS has to focus on improving its collaboration with the human driver. The driver’s cognitive state can be inferred from his/her behaviors, which can improve human-machine collaboration and thus driving safety.

Uncertainty Transportation Diagnosis Digital Twin
Zhihao Jiang
Zhihao Jiang
Assistant Professor

Zhihao Jiang is the director of Human-Cyber-Physical Systems Lab at ShanghaiTech University.

Renzhi Tang
Renzhi Tang
Developer

Renzhi Tang is a Computer Science M.S graduate Class 2022 at ShanghaiTech University. He is currently a developer at GuardStrike (深信科创)

Junyu Diao
Junyu Diao
Alumni

Junyu Diao graduated from ShanghaiTech University and is now with Shengqu Games.

Yi Gu
Yi Gu
Alumni

Yi Gu graduated from ShanghaiTech University and is now with COMAC.

Related

Posts

Patent Granted by CNIPA
Our patent “Driving Assistance Method and System, Medium and Terminal” was granted by CNIPA.
Zhihao Jiang, Renzhi Tang
Aug 30, 2022
Project
Patent Granted by CNIPA
Multiple US Patents granted by USPTO
Patents were applied when I was with Toyota ITC
Zhihao Jiang
Last updated on Jul 13, 2021
Project
Multiple US Patents granted by USPTO

Publications

Ethics by Design in Autonomous Driving: Developing a Model-based Liability Determination Framework
A Model-based Liability Determination Framework for traffic accidents.
Yichuan Yu, chenyang, Zhihao Jiang
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A cognitive digital twin approach to improving driver compliance and accident prevention
Introduce personalized driving assistance using cognitive digital twins.
Yi Gu, Shuhang Li, Bangzheng Fu, Renzhi Tang, lifeng, sen, Zhihao Jiang
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Cognitive-Digital-Twin-Based Driving Assistance
A Cognitive-digital-twin-based Driving Assistance System (CDAS) that issues warnings tailored to the driver’s perception of the driving environment and driving style.
Junyu Diao, Renzhi Tang, Yi Gu, Sen Tian, Zhihao Jiang
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Stable Interaction of Autonomous Vehicle Platoons with Human-Driven Vehicles
This paper studies emergency-braking interactions between autonomous platoons and nearby human-driven vehicles. It derives head-to-tail …
M. Pirani, Yining She, Renzhi Tang, Zhihao Jiang, Y. Pant
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Stable Interaction of Autonomous Vehicle Platoons with Human-Driven Vehicles
US Patent: Digital behavioral twin system for intersection management in connected environments
CW Lin, Zhihao Jiang
Project Source Document
US Patent: Digital behavioral twin system for intersection management in connected environments
US Patent: Context system for improved understanding of vehicle-to-everything (V2X) communications by V2X receivers
H Kobayashi, Zhihao Jiang
Project Source Document
US Patent: Context system for improved understanding of vehicle-to-everything (V2X) communications by V2X receivers
US Patent: Vehicle data reservation service
BG Kim, Zhihao Jiang
Project Source Document
US Patent: Vehicle data reservation service
Driver's Perception Model in Driving Assist
Identify driver’s perception model from observing behaviors to provide more accurate driving assistance.
Renzhi Tang, Zhihao Jiang
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Driver's Perception Model in Driving Assist
US Patent: Collision avoidance for a connected vehicle based on a digital behavioral twin
Zhihao Jiang, S Shiraishi, BG Kim
Project Source Document
US Patent: Collision avoidance for a connected vehicle based on a digital behavioral twin
Digital Behavioral Twins for Safe Connected Cars
Driving is a social activity which involves endless interactions with other agents on the road. Failing to locate these agents and …
X Chen, E Kang, S Shiraishi, VM Preciado, Zhihao Jiang
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Digital Behavioral Twins for Safe Connected Cars
Property-Driven Runtime Resolution of Feature Interactions
The feature interaction problem occurs when two or more features interact and possibly conflict with each other in unexpected ways, …
SG Raghavan, K Watanabe, E Kang, CW Lin, Zhihao Jiang, S Shiraishi
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Property-Driven Runtime Resolution of Feature Interactions