We will apply our results from neuroscience and safe control to improve driver-assistance technology as follows. First, we will study the use of visual attention information to detect risks early, before the failure to detect risk-critical obstacles can be identified from the drivers' control action and vehicle states. Second, we will find safer control actions even when conventional methods, which are unnecessarily conservative, become infeasible in finding safe intervention strategies.
In this proposal, we will explore the application of our parallel projects on neuroscience and safe control theory on driver-assistance technology and, ultimately, autonomous driving technology. The application is expected to improve the current driver-assistance technology in the following two perspectives.
Conventional driver-assistance is triggered by the vehicle state. In contrast, we will study the use of visual attention information to detect risky events before they can be reflected on the control action or vehicle state. Human drivers must first identify risk-critical obstacles before they can change their control actions with the delay in sensorimotor control, and the control action also needs time to be reflected on the vehicles’ states. This suggests the potential use of visual attention to detect risky events much earlier than when it is reflected in the vehicle state. We aim to achieve early interference of driver-assistance technology in risky events by estimating if human drivers have failed to recognize risk-critical objects based on their visual attention trajectories. Conventionally, such information was believed to be detectable only from the brain signal. The detection of brain signals requires expensive sensors which are considered to be not affordable in standard driver assistance systems. However, recent advancement in neuroscience suggests that such information could be contained in eye movement information. Our parallel project on neuroscience with experts in neuroscience (Dr. Minoru Nakayama, Dr. Sakurada Takeshi) and automobile control (Dr. Fukao Takanori) explore the metrics to quantify if human drivers have recognized certain risk-critical objects based on eye movement measurements only. The proposed projects will study its possible use to enable the early detection of risky events in driving assistance technology.
The driver-assistance technology and safe planning techniques in autonomous vehicles usually use conventional safe control methods that are unnecessarily conservative. These methods attempt to provide safe solutions for all possible worst-case scenarios and thus often cannot find feasible solutions in many risky scenarios. For example, humans are able to cut into the middle of two vehicles in close proximity, while such methods cannot find safe trajectories to do so. Motivated by the need for less conservative safe control methods, we have developed a set of tools that are efficient enough to compute the safe probability and/or find control actions with high safe probability in real-time. Such methods take a stochastic framework and find safer actions even when the conventional safe control method ends up in dead-lock and infeasible solutions. This proposal will explore the application of such methods in driver-assistance technology and safe planning techniques for autonomous vehicles.
The milestones of the proposed project are modeling, experiments, and verification. We will aim to complete these milestones in months 1-6, months 3-8, and months 6-12.
Industry partnership: We will jointly investigate this project with Jin Ge (Toyota Research Institute). She holds research projects on Toyota Guardian Driver Assist System, which we will use as use cases. The algorithms developed at Nakahira’s group will also be made publicly available so that more researchers and engineers can build upon our framework.
Educational outreach: PI Nakahira is teaching two classes: fundamentals of control and autonomous control systems. The insights obtained in this project will be disseminated in the courses as motivating examples to boost students’ interests in learning control techniques and the human sensorimotor control system.
Expected Accomplishments and Metrics
By quantifying the visual attention for safe driving, we expect to improve the safety feature of driving assistance technology by enabling earlier interference. This early interference is achieved by estimation if human drivers have failed to recognize risk-critical objects based on their visual attention trajectories. Additionally, by applying our recent techniques from stochastic safe control methods, we expect the driving assistance system and/or autonomous vehicle control to find a safer action in highly risky scenarios when conventional safe control methods would yield infeasible solutions. Ultimately, these technologies can lead to affordable and safe driver assistance and autonomous driving technology.
||Carnegie Mellon University
||Carnegie Mellon School of Engineering
||Staff - Business Manager
||Carnegie Mellon School of Engineering
||Faculty - Untenured, Tenure Track
Amount of UTC Funds Awarded
Total Project Budget (from all funding sources)
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|Toyota Research Institute
||Deployment Partner Deployment Partner