Project
AI-Powered In-Vehicle Experience
Project Under NDA
Client names, data, and design assets for this showcase are modified or hidden to ensure confidentiality and protect sensitive information.
Year
2025
Duration
8 WEEKS
Awards & Accolades:
ACE Award
9.5/10 Client Satisfactory Rating
Product Design
In-Car UI
Goal: Designing an Intelligent, Adaptive Drive Experience
To envision an AI-powered in-vehicle experience for Halo that anticipates driver needs, reduces cognitive load, and creates a seamless, adaptive driving environment. Within an accelerated 8-week timeline, the project focused on designing two interconnected applications—Halo Assist and Halo Cabin—to reimagine how intelligence, interaction, and environment come together inside the car. The goal was not just to build features, but to define a forward-looking experience where the vehicle behaves as an intuitive, context-aware companion.
From Ideas to a Working Experience
The process was structured to bring clarity to a complex, AI-driven ecosystem within a compressed timeline. It focused on breaking the experience into distinct stages—moving from defining intent to visualizing behavior, and finally shaping a cohesive, testable solution across both applications.
Outlining Feature List: Established a focused feature set aligned with driver needs, system capabilities, and overall experience vision.
Mapping Scenarios & User Stories: Mapped key driving scenarios and user stories to capture triggers, behaviors, and expected system responses.
Defining Flows & Storyboarding: Translated scenarios into detailed flows and storyboards to visualize end-to-end interactions across driving moments.
Designing & Prototyping: Explored interaction models, UI patterns, and AI responses through rapid wireframing and high-fidelity prototypes.
Testing & Iterating: Validated concepts through scenario-based walkthroughs, refining clarity, timing, and cognitive load.
This approach ensured that every decision was grounded in real-world context while enabling rapid iteration. By continuously connecting features, scenarios, and interactions, the experience evolved as a cohesive system—balancing intelligence, usability, and feasibility.
Two seamlessly connected systems—one that thinks ahead, and one that adapts around it—coming together to create a truly intelligent in-car experience.
Two Systems, One Cohesive Experience
The solution came together through two tightly integrated applications—Halo Assist and Halo Cabin.
Halo Assist acts as the intelligence layer, designed to provide proactive, context-aware support throughout the journey. It anticipates needs such as navigation adjustments, traffic awareness, and contextual recommendations, shifting the interaction from reactive commands to intelligent assistance.
Halo Cabin focuses on the in-car environment, shaping a responsive and adaptive interface across cockpit displays. It brings together information, controls, and ambience into a cohesive system that adapts to driving conditions and user context. From dynamic UI states to personalized cabin presets, the experience ensures that both intelligence and environment work seamlessly together rather than as separate systems.
Building Intelligence into Every Interaction
The experience was shaped around a set of core design principles that ensured intelligence remained helpful, intuitive, and non-intrusive. These principles guided how AI behaves, how information is surfaced, and how users interact with the system in real driving conditions.
Multimodal by Default: Combined touch and voice to enable seamless, low-effort interactions without over-reliance on a single input method.
Familiar, Physical Interactions: Digital controls were inspired by real-world elements like sliders and knobs, making interactions intuitive and reducing the learning curve.
Minimal Effort, Maximum Clarity: Key tasks were designed to be completed within three taps, ensuring speed and ease without compromising safety.
Adaptive UI Behavior: Interface dynamically adjusted based on driving conditions and cognitive load, balancing information density and focus.
Contextual Alerts & Hierarchy: Information was structured to surface critical alerts at the right time, while keeping secondary inputs unobtrusive.
Predictive & Learning AI: The system continuously learned from user behavior, preferences, and vehicle data to provide proactive, relevant suggestions.
Feedback Before Action: System communicated intent before executing physical changes (like climate or windows), building trust and preventing errors.
These principles ensured that every interaction—whether initiated by the user or the system—felt natural, predictable, and aligned with the realities of driving, creating an experience where intelligence supports rather than interrupts.
