Image

Das von Acsia betreute Team von Ingenieurstudenten gewinnt den Sicherheitspreis beim Shell Eco-Marathon

Oct 17, 2022
Team Pravega 01 Acsia
At the renowned Shell Eco-Marathon 2022 in Indonesia's Pertamina Mandalika circuit, Team Pravega's car "Vandy," guided by Acsia Technologies, clinched the highest accolades.

Vandy’ recognized for innovation and safety in electric vehicle design.

Thiruvananthapuram, 17. Oktober 2022: Acsia Technologies, ein weltweit führender Anbieter von Automobilsoftware für vernetzte Fahrzeuge, Infotainmentsysteme und E-Mobilität, gab heute bekannt, dass das von Acsia betreute Team des Government Engineering College, Thiruvananthapuram, beim prestigeträchtigen Shell Eco-Marathon (SEM) 2022 auf der Pertamina Mandalika Rennstrecke in Indonesien den ersten Platz belegt hat.

Ein Elektroauto namens ‘Vandy’, das von den Ingenieursstudenten entworfen wurde, hat den International Award for Safety von Dupont gewonnen und eine lobende Erwähnung in der Kategorie International Award for Technical Innovation erhalten. Die Ministerin für Hochschulbildung der Regierung von Kerala, Dr. R. Bindu, spielte eine entscheidende Rolle bei der Erleichterung des Zugangs des Studententeams zu Stipendien und Programmen, was die gemeinsame Anstrengung hinter dieser Leistung unterstreicht.

Das Team mit dem Namen Pravega, bestehend aus 19 Studenten der Fachrichtung Maschinenbau der Hochschule, zeigte während des gesamten Projekts außergewöhnliches Talent und Engagement. Ihr Elektroauto stach unter den Bewerbern aus aller Welt hervor und wurde nach strengen Befragungen und Tests zum Klassenbesten gekürt. Mit einer Höchstgeschwindigkeit von 27 km/h und einem Gewicht von etwa 80 kg symbolisiert Vandy nachhaltige Technik und Umweltbewusstsein.

Kalyani S. Kumar, Teamleiter von Pravega, bedankte sich für die erhaltene Unterstützung: “Wir haben von Acsia Technologies unschätzbare Unterstützung erhalten, die es uns ermöglicht hat, unsere Vision eines umweltfreundlichen Fahrzeugs zu verwirklichen.” Jijimon Chandran, Gründer und CEO von Acsia Technologies, fügte hinzu : “Wir sind begeistert, die bemerkenswerten Leistungen der Studenten zu sehen. Acsia freut sich, an ihrem Erfolg beteiligt zu sein und verpflichtet sich, auch weiterhin junge Talente zu fördern und Innovationen im Ingenieurwesen voranzutreiben.”

Die Inspiration für das Design von Vandy stammt von der Biomimikry der Tigerhaie, die recycelte und biologisch abbaubare Materialien in ihre Konstruktion einfließen lassen. Das Fahrzeug verfügt über ein innovatives Batterie-Wärmemanagementsystem, das von dem Team entwickelt wurde und nachhaltige Energietechnologien und -bewertungen nutzt, wie in einem Forschungspapier veröffentlicht wurde. Darüber hinaus unterstreichen fortschrittliche Sicherheitsfunktionen, darunter ein KI-gestütztes Schläfrigkeitserkennungssystem, den Fokus des Teams auf technologische Innovation und Benutzersicherheit.

Der Erfolg von Team Pravega unterstreicht die Bedeutung der Zusammenarbeit zwischen Wissenschaft, Industrie und Regierung bei der Förderung von Innovation und Nachhaltigkeit. Das von verschiedenen Organisationen und Institutionen, darunter Acsia Technologies, Canara Bank und Red Maple Realtors, unterstützte Projekt ist ein Beispiel für das Potenzial der interdisziplinären Zusammenarbeit, um den Fortschritt voranzutreiben und globale Herausforderungen zu bewältigen.

Die lobenswerten Bemühungen von Dr. Bijulal D, dem Direktor des Government Engineering College, Dr. Bindu Kumar, dem Leiter der Abteilung Maschinenbau, und Dr. Anish K. John, dem Berater der Fakultät, trugen ebenfalls zum Erfolg des Projekts bei und spiegeln das gemeinsame Engagement für Spitzenleistungen in Bildung und Forschung wider.

Der Shell Eco-Marathon dient als Plattform für Studenten auf der ganzen Welt, um ihren Einfallsreichtum und ihre Kreativität bei der Entwicklung energieeffizienter Fahrzeuge unter Beweis zu stellen. Die Leistung von Team Pravega bringt nicht nur Anerkennung für ihre Alma Mater, sondern unterstreicht auch die wachsende Bedeutung Indiens auf dem Gebiet der nachhaltigen Automobiltechnik.

Presse Kontakt

Athul Lal A G
Direktor für PR
Email: athul.lal@acsiatech.com
Mob: +91 81290 07793

Don’t miss an update!
Popular PRs
Acsia Technologies Powers Central Institute on Mental Retardation (CIMR) with Solar Energy
READ MORE ABOUT
CIMR Solar Project
Acsia Technologies Unveils New Copilot LiLA
READ MORE ABOUT
LiLA Acsia Copilot
BMW’s EV Pioneer Stefan Juraschek Engages Kerala’s Emerging Engineers at Acsia Campus Connect
READ MORE ABOUT
Acsia Youth Connect
Acsia Technologies Inaugurates Global Headquarters and Research & Development Centre and Launches a Generative AI-based Developer Assistant for Automotive Software
READ MORE ABOUT
Acsia Inauguration
Acsia Technologies Onboards BMW Veteran Stefan Juraschek as Strategic Advisor
READ MORE ABOUT
Stefan Juraschek appointed as Strategic Advisor 02 Acsia
Acsia Technologies has named Mr. Stefan Juraschek, a former BMW expert, as their new Strategic Advisor.
Request a Meeting
AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.