Contextual Intelligence in Automotive Performance UX

Role: Interaction / UX Designer
Domain: Automotive HMI
Client: Mahindra & Mahindra
Timeline: 10-12 months
Team: Research, UX, UI, Engineering, Rapid Prototyping

Overview

This project focused on designing a performance-oriented in-car application for Mahindra vehicles, with contextual intelligence as the core design principle.

Automotive interfaces operate in a partial-attention, safety-critical environment. Unlike mobile or web products, drivers do not interact with the system continuously. Their attention dynamically shifts based on speed, road conditions, intent, and driving mode.

The objective of this project was not to design a visually rich interface, but to design a context-aware system that:

  • Delivers information only when it is relevant

  • Aligns with the driver’s intent and mental state

  • Supports performance driving without increasing distraction

  • Remains feasible within real-world engineering and production constraints

The success of the design depended less on what was shown, and more on when, where, and why information appeared.

Problem Statement

How might we design a contextually intelligent performance interface that:

  • Helps drivers feel connected to vehicle performance

  • Adapts to changing driving conditions and user intent

  • Minimises cognitive load during high-attention moments

  • Avoids unnecessary interaction while driving

  • Can evolve as features and requirements change over time

Users

Primary Users

  • Car drivers aged 25–55

  • Male and female

  • Indian users

  • Performance-oriented drivers who actively monitor vehicle behaviour

User Context

  • Primary focus on the road at all times

  • Very short interaction windows

  • Interactions are often glance-based

  • Zero tolerance for confusion or visual clutter

  • Expect the system to support them silently, not demand attention

Understanding user context was more important than understanding user preference.

Constraints

  • Driver distraction and safety regulations

  • Fixed in-vehicle hardware constraints (screen size, resolution, input methods)

  • Continuously evolving engineering requirements

  • Tight and shifting timelines

  • Dependency on multiple internal teams (engineering, UI systems, animation, 3D)

  • Requirement for production-ready designs, not conceptual explorations

These constraints reinforced the need for a context-first design approach.

Key Challenges

Designing for Context, Not Screens

A major challenge was shifting the mindset from designing static screens to designing context-responsive behaviour.

The same information could be:

  • Critical in one driving scenario

  • Distracting or unnecessary in another

This required prioritising situational relevance over feature completeness.

UX vs Aesthetics Trade-off

A key learning was the inherent tension between usability and visual richness.

  • Reducing interaction steps often required simplifying visuals

  • Increasing aesthetic complexity risked higher cognitive load

In this project, aesthetics were treated as a secondary layer, subordinate to clarity, timing, and glanceability.

Constantly Evolving Requirements

Requirements were not fixed. My responsibilities included:

  • Actively extracting requirements from engineering teams

  • Creating the right questions to uncover contextual dependencies

  • Maintaining a living requirements list

  • Designing systems flexible enough to absorb future changes

Contextual intelligence helped prevent redesigns by enabling adaptive structures instead of rigid layouts.

Design Approach

Contextual Intelligence as the Core Principle

The interface was designed around the belief that:

Not all information is useful at all times.

The focus shifted from feature availability to contextual relevance, guided by:

  • Driver intent

  • Attention availability

  • Driving conditions

  • Performance-focused moments vs normal driving moments

This ensured the system supported the driver without demanding engagement.

Understanding the Existing System

Before proposing improvements, I analysed:

  • Why the current feature existed

  • What problem it was originally designed to solve

  • Why it was structured the way it was

This helped ensure future designs were evolutionary, respecting existing mental models while improving contextual relevance.

Context-Aware Information Architecture

I worked on improving the system architecture by:

  • Re-evaluating feature access points

  • Reducing unnecessary navigation depth

  • Surfacing critical information at moments of relevance

  • Hiding or deprioritising information during high-focus driving situations

The interface was designed to be present when needed and invisible when not.

Bridging Research, Design & Engineering

To align teams around context-driven decisions, I introduced a Trigger–Input Table that:

  • Mapped user context and intent to system behaviour

  • Translated design logic into engineering-readable inputs

  • Created a shared understanding of why features appeared in specific contexts

This artifact became a key tool for cross-functional alignment.

Designing for Production Reality

Every design decision was evaluated through a production and engineering lens.

I created and coordinated:

  • Global UI component requests

  • Local component requests

  • UI icon requests

  • 2D animation requests

  • 3D animation and render requests

  • Asset handoff to rapid prototyping engineers

Designing with assets and developer handoff in mind ensured smooth translation from concept to prototype.

Process

  • Competitive benchmarking with focus on contextual behaviour

  • Mind mapping relationships between features and driving scenarios

  • Rapid wireframing

  • Iterative prototyping

  • Continuous design tracking

  • Strict version control to manage evolving assets and files

The production process itself was built and refined during the project.

Outcomes

  • A context-aware performance UX framework

  • Improved clarity and reduced cognitive load for drivers

  • Stronger alignment between design, engineering, and prototyping teams

  • A scalable system capable of adapting to future performance features

  • Clear asset and component direction for production teams

Key Learnings

  • Contextual intelligence is more valuable than feature richness in automotive UX

  • The best in-car interfaces know when not to speak

  • Designing for cars requires thinking in states, intent, and behaviour, not screens

  • UX designers in automotive must act as system architects

  • Empathy is required not only for users, but also for engineers and stakeholders

  • Designing with context and production in mind reduces rework and friction

Reflection

This project reinforced that automotive UX is a high-responsibility design discipline.

You are not designing for convenience—you are designing for attention, safety, and trust. Contextual intelligence became the most critical tool in ensuring that the system supported the driver without overwhelming them.

The experience strengthened my ability to design adaptive, context-aware systems within complex, constraint-driven environments.