Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange naCansu Cetin

Dhizaini Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange naCansu Cetin ndiye mukundi anokudzwa zvikuru wemubairo unoremekedza we2025 Mubairo weSimbi we A' Design Award muChikwata cheKukwikwidza cheChiso, Kushandira Pamwe neRuzivo rweMushandisi.

SimbiChiso· 2025

Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange

na Cansu Cetin

Mutengi: Driven AVS

Mukundi weMubairo weSimbi we A' Design Award muChikwata cheChiso

12,994

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Shanyira Saiti Ona pa A' Design Award Mashoko Enhau Bheji reMubairo

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Dhizaini

Nezvekwa Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange

Chirongwa ichi chinotsanangura patsva ongororo yemujaho wenhangemutange nekugadzira chishandiso che telemetri chinonyatsonzwisisika chinobatanidza mujaho wemunhangemutange neyechokwadi. Chinoita kuti data yakaoma ive nyore kubudikidza nemepu dzenzira dzinoshanda, madhashboard anogona kugadziriswa, uye kuenzanisa makutenderera. Chakagadzirwa nemhinduro dzinodzokororwa kubva kuvatyairi vemujaho wenhangemutange, dhizaini iyi inopa ruzivo rwakadzama asi rwunosvika kuti rwuvandudze maitiro. Ichibatanidza hutsva nekushandiswa zvakanaka, inosimbisa vatyairi kuti vaenderere mberi vachivandudza uye kunzwisisa zvirinani data yavo yemujaho.

Kuzivikanwa

Nezve Mubairo weSimbi we A' Design Award

Mubairo weSimbi we A' Design Award wakahwinwa ne Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange, rukudzo runokudzwa runopiwa kumadhizaini akanaka anozadzisa zvinodiwa zvehunyanzvi neyeindasitiri zvinotarisirwa kuzvigadzirwa zvakagadzirwa zvakanaka. Vakunda veSimbi vanoratidza unyanzvi hukuru, hunyanzvi hwakakwirira, kunzwisisa kwevakachenjera uye kugona kwekugadzira kwevagadziri vavo. Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange inotarisirwa kuva chigadzirwa chinokudzwa zvakanyanya, chinoremekedzwa uye chinodiwa, chinoshanda uye chine zvitsva. Dhizaini iyi yakahwina Mubairo weSimbi we A' Design Award inosanganisa maitiro akanakisa eindasitiri uye ine hunhu hwetekinoroji hwakakwana hwakabatana neruzivo nehunyanzvi. Nekushanda kwayo zvakanaka, Race Data AI Telemetri Yevatyairi Vemujaho Wenhangemutange inoremekedzwa uye yakagadzirirwa kupa kuvandudzwa kwehupenyu kunobatsira kuita kuti nyika ive nzvimbo iri nani.

Shure kweDhizaini

Bepa Renhau

Zvikwereti, nhengo dzetiimu nezvimwe zvakadzama zvakapihwa nemudhizaina.

Hunhu Hwakasiyana: This design transforms sim racing analysis with intuitive data visualization and advanced telemetry tools, providing racers with actionable insights. Its user-focused approach ensures accessibility while maintaining technical depth, featuring interactive track maps, customizable dashboards, and seamless lap comparisons. Personalized analysis tools and community-driven feedback make it a standout solution for improving performance and bridging the gap between virtual and real racing.
Kukura & Kupakiwa: The design was developed with a focus on responsiveness, ensuring compatibility across various screen sizes. The layout adapts seamlessly from 1024x768px to 1792x1024px for larger displays. Interactive elements were optimized for touch and mouse input, with clickable areas sized for ease of use across devices. Data visualization tools were engineered for high performance, processing telemetry data in real-time with minimal latency, ensuring smooth user experience even with large datasets.
Matagi: Telemetry analysis, real-time data processing, racing insights, sim racer tools, user experience design, user-centric design,
Tekinoroji Yekugadzira: The design was developed using an iterative UX/UI process, emphasizing user feedback from sim racers. Wireframes and prototypes were created to test and refine key functionalities, such as telemetry data visualization and lap comparisons. Agile methodology was employed to allow for continuous improvement based on user testing. A modular design approach ensured flexibility, while data-driven insights guided the creation of interactive features that prioritize performance optimization and ease of use.
Changamire yeDhizaini: The hardest part of this design was simplifying complex telemetry data without losing essential details. The challenge lay in balancing technical depth with user accessibility, as racers needed both detailed insights and easy navigation. Historical perspectives of traditional racing tools influenced the design, but new technology offered opportunities to innovate. External factors, such as varying levels of user expertise and limited research on specific racing behaviors, also presented obstacles, requiring extensive user feedback to refine the product.
Tsvakurudzo: The research combined qualitative and user-centered studies to understand sim racers' needs and behaviors. The goal was to simplify telemetry data presentation to enhance performance. Insights were gathered through interviews, surveys, and online community observations, involving both casual and competitive sim racers. Results revealed a preference for simplified, interactive interfaces, informing a design that improves performance and user experience in virtual and real-world racing.
Chikurudziro: The project was inspired by the challenges sim racers face in translating virtual performance to real-world racing. Recognizing the complexity of existing telemetry tools, the goal was to simplify data analysis while enhancing usability. Drawing from user interviews, market research, and technical studies, the design combines intuitive features with a user-centered approach to help racers optimize performance and make the most of limited practice time.
Nguva yePurojekiti: The project started in May 2024 in Turkey and finished in December 2024 in Canada.
Mafambiro eBasa: This design helps sim racers improve their performance by providing easy-to-understand data about their racing. Users can view their racing line, speed, and other key information to see where they can improve. By comparing laps, racers can find areas to optimize, such as braking and acceleration points. The design allows for simple interactions like clicking on turns to get more detailed feedback, making it easier to focus on key areas for improvement and achieve better results over time.