Vanchha Chandrayan

As vehicles become increasingly intelligent and assistive, the challenge of designing intuitive, safe, and low-distraction interaction methods becomes more critical. Voice, visual, and touch interfaces, although common and widely used, can be intrusive or unreliable in certain situations. Electromyography (EMG) sensors offer a promising alternative by detecting subtle muscle activations that reflect a driver's intent or emotional state. These muscle-based gestures can enable silent, quick, and low-distraction communication between driver and vehicle systems, supporting a more natural, intuitive, and adaptive human-vehicle interaction.

In this project, students will explore how EMG-based gestures can be leveraged to improve in-vehicle interaction and feedback. They will design and investigate how such gestures can support communication and feedback integration with an LLM-based in-cabin assistant. This includes how drivers might express intent, preferences, or reactions to system assistance through adaptive gestures; and how LLMs can interpret or adapt to such inputs to enable natural and context-aware interactions. Students will analyze how this modality for interaction affects overall usability, trust, and satisfaction, and examine how it complements/interferes with other modalities like voice or touch.

Emotions like anger, frustration, or anxiety can compromise driving performance by impairing situational awareness, reaction times, and decision making. Recent advancements in large language models (LLMs) have shown reasoning capabilities in detecting complex emotions using multiple modalities. By integrating multimodal sensor data and contextual driving information, LLMs can also be leveraged to detect vehicle occupant emotions and trigger actions to mitigate their impact on driving performance.

In this project, the students will design and prototype an automotive in-cabin assistant to detect driver emotions and respond through context-aware recommendations to support safer driving. The system will utilize multimodal inputs (voice, facial features, heart rate, driving behavior), contextual signal like traffic conditions, driving duration, weather conditions. Based on this information, the assistant will generate appropriate responses such as offering calming suggestions, initiating empathetic conversations. Students will evaluate system’s usability, perceived appropriateness and supportiveness of the system, and investigate how LLM-driven emotion recognition and context-aware responses can enhance human-vehicle interaction.