Driving the Future: Autonomous Vehicles and the Road to Smarter Mobility in Hyderabad

By Dr. Annu,Affiliated Researcher, WAI Labs

https://annuiith.blogspot.com/

Hyderabad's Urban Mobility Challenges

Hyderabad has rapidly emerged as one of India's technology centers, hosting major IT campuses, pharmaceutical industries, and being an engine of growth for the region. However, the city's transportation infrastructure is under increasing strain due to rapid urbanization and motorization. Some of these challenges include:

• Soaring Vehicle Numbers: Greater Hyderabad has more than 8.5 million registered vehicles, with registrations increasing continuously every year, placing immense pressure on road infrastructure [1].

• Severe Traffic Congestion: According to the TomTom Traffic Index 2024, Hyderabad ranks among India's most congested cities, with commuters spending nearly 32 minutes to travel just 10 km [2].

• Road Safety Emergency: India records 100,000s of road fatalities annually, with ~200 In Cyberabad alone in 2025, highlighting the vulnerability of non-motorized road users.

• Dominance of Private Vehicles: Personal vehicles account for the overwhelming majority of vehicles in the Hyderabad metropolitan region. Earlier estimates indicate that over 70 lakh of the 77 lakh registered vehicles are private vehicles, mainly two-wheelers and cars, while commercial vehicles, cabs, and goods carriers constitute only a small fraction [3]. Such heavy dependence on private mobility exacerbates congestion and inefficient road utilization.

• Imbalance in Transport Modes: Hyderabad's mobility ecosystem remains highly dependent on road-based transport, whereas public transport systems—including buses and metro rail—carry a relatively smaller share of daily trips. Limited bus availability, first- and last-mile connectivity issues, and rising ownership of personal vehicles have gradually shifted commuters away from public transport, intensifying congestion and environmental impacts [5].

• Environmental Concerns: Transportation is one of the major contributors to urban air pollution. Recent studies indicate that road transport contributes nearly 24% of nitrogen dioxide (NO₂) emissions in Hyderabad, while vehicular emissions account for a significant share of particulate matter and sulfur dioxide concentrations within the Greater Hyderabad Municipal Corporation (GHMC) area [4]. Increasing motorization has consequently become a major challenge for sustainable urban development.

These challenges collectively underscore the need for broadening of transport infrastructure. Traditional solutions—flyovers, metro lines, stricter policing—are necessary but not sufficient. Clearly, Hyderabad is at an inflection point. The city needs smarter, safer, and more efficient mobility systems - Intelligent and autonomous transportation systems that are capable of improving traffic efficiency, enhancing road safety, and reducing emissions must be an option.

While these challenges are not unique to Hyderabad and are commonly faced by many megacities; differences in regulations, urban density, travel patterns, and socio-economic conditions necessitate localized approaches. Nevertheless, several strategies and technological solutions developed worldwide can be adapted and shared to address these common urban mobility challenges.

Role of Autonomous Vehicles(AVs) in safer and sustainable future transport for Hyderabad

There is no panacea to alleviate these problems, but a combination of approaches for the city to a transport system that is responsive to the needs of the population, while being durable, cost-aware and cleaner. We discuss the role of AVs, powered by advanced AI and V2X (vehicle-to-everything) communication, in this future transport system for Hyderabad city, possibilities and limitations. 

Some of the benefits could be :

• Road Safety Improvements: AVs that combine perception systems (camera, radar, LiDAR) with V2X alerts can prevent accidents, and adhere strictly to rules of the road. 

• Time & Productivity Gains: AV fleets coordinated through V2X can smooth traffic flow, reducing travel delays and improving productivity for both commuters and logistics.

• Cleaner, Sustainable Mobility Coordinated driving reduces idling, unnecessary braking, and emissions, supporting Hyderabad’s green mobility targets.

• Tech Ecosystem Readiness: Hyderabad has the rare combination of TiHAN, a thriving AI and startup ecosystem, and ongoing 5G deployment, making it a promising candidate for piloting connected and autonomous mobility solutions in India. Established at IIT Hyderabad under the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS), TiHAN (Technology Innovation Hub on Autonomous Navigation) is India's first autonomous navigation testbed and provides a unique platform for developing, testing, and validating connected and autonomous vehicle technologies under Indian conditions. The testbed has already been used to evaluate autonomous navigation, perception, and communication systems in controlled and semi-structured environments.

The Key Technical Challenges

Deploying AVs in India is considerably more challenging than in regions with more homogeneous traffic patterns. Hyderabad's roads mirror the broader complexity of Indian mobility, where diverse road users, dynamic traffic conditions, and unstructured driving behaviors make edge cases as seen in existing datasets the norm rather than the exception.

1. Perception in Chaotic Traffic

AVs rely heavily on accurate perception of their surroundings. However, Indian roads comprise a heterogeneous mix of buses, trucks, auto-rickshaws, two-wheelers, cyclists, jaywalking pedestrians, street vendors, and even stray animals. Frequent occlusions, poor lane discipline, varying weather conditions, and inadequate road markings make scene understanding particularly difficult for vision-only systems.

Possible Solution: Robust multi-sensor fusion combining cameras, radar, and LiDAR can provide complementary information and improve reliability. Equally important is the development of India-specific datasets containing diverse scenarios such as night driving, dust, rain, heavy traffic, and partially obstructed objects to train AI models capable of handling local conditions.

2. Localization Under Uncertainty

Precise localization is fundamental to autonomous driving. In urban environments, GPS signals can suffer from multipath effects caused by tall buildings, while frequent roadworks, temporary diversions, and rapidly changing infrastructure can render static maps inaccurate.

Possible Solution: Hybrid localization approaches combining GNSS with RTK corrections, inertial sensors, visual odometry, radar-based localization, and continuously updated high-definition maps can improve positioning accuracy and robustness in challenging environments.

3. Reliable Connectivity

Cooperative autonomous driving requires vehicles to exchange safety-critical information with very low latency and high reliability. Applications such as collision avoidance, emergency braking, and pedestrian crossing alerts demand communication delays of well below 100 ms. Maintaining such performance in dense urban environments remains challenging.

Possible Solution: Cellular Vehicle-to-Everything (C-V2X) communication, standardized by 3GPP Release 16 and beyond, together with strategically deployed roadside units and Multi-access Edge Computing (MEC) infrastructure, can provide the low-latency communication required for cooperative awareness and real-time decision-making.

4. Standards and Spectrum Alignment

Interoperability is essential for large-scale deployment of connected and AVs. Fragmented standards and incompatible communication technologies can hinder ecosystem development and increase deployment costs.

Possible Solution: India has adopted C-V2X in the 5.9 GHz Intelligent Transportation Systems band. Therefore, future deployments and pilot projects should align with evolving 3GPP standards to ensure compatibility, scalability, and long-term sustainability.

5. Human Factors in Mixed Mobility

Traffic interactions in India are often governed by implicit social cues and highly adaptive driving behaviors rather than strict adherence to traffic rules. AVs must therefore understand and anticipate the actions of aggressive drivers, pedestrians, cyclists, and two-wheelers operating in close proximity.

Possible Solution: AI models must be trained using diverse, representative datasets collected under Indian traffic conditions. Advanced prediction and behavior modeling techniques are required to enable autonomous systems to safely coexist with heterogeneous road users while maintaining fairness and robustness.

My Research Contributions: Building the Foundations for Connected and Autonomous Mobility

As a Prime Minister's Research Fellow at IIT Hyderabad and a Women in AI-affiliated researcher, my work focuses on one of the key enablers of autonomous transportation: reliable and intelligent Vehicle-to-Everything (V2X) communication. While AVs often attract attention for their perception and AI capabilities, safe and scalable deployment also requires robust wireless connectivity, efficient spectrum utilization, low-latency communication, and real-world datasets representative of Indian traffic conditions. My research addresses these challenges through analytical modeling, optimization, machine learning, and experimental validation.

Tackling Network Congestion in Dense Traffic

As the number of connected vehicles increases, wireless channels become congested, resulting in packet collisions and delayed safety messages. Such delays can compromise applications such as emergency braking and cooperative collision avoidance.

To address this problem, I have developed congestion-aware resource management techniques for V2X communication. Our probabilistic congestion control framework, recognized with the Best Paper Award at IEEE ANTS 2023, enables vehicles to adapt their transmissions under heavy traffic loads, thereby improving reliability and maintaining timely delivery of safety-critical information.

Improving Resource Allocation for Cooperative Driving

AVs operating in platoons and dense urban traffic require efficient sharing of radio resources. Existing scheduling approaches often suffer from interference and packet collisions under highly dynamic traffic conditions.

My research has introduced queue-aware and distance-aware scheduling mechanisms for Semi-Persistent Scheduling (SPS) in 5G-V2X networks. Presented at IEEE VTC 2024, this work incorporates traffic dynamics and inter-vehicle spacing into the scheduling process, enabling more reliable communication among closely spaced vehicles and supporting cooperative driving scenarios.

Prioritizing Emergency and Public Transportation

Not all messages transmitted in vehicular networks have equal importance. Information originating from ambulances, emergency responders, or public transportation vehicles often requires higher priority and lower latency.

To support such scenarios, I developed a NOMA-based priority transmission framework for V2X communication, presented at IEEE VNC 2023. The proposed approach allows high-priority vehicles to receive preferential access to communication resources, improving the responsiveness of intelligent transportation systems during emergencies.

Enabling Real-Time Decision Making Through Edge Computing

Future AVs continuously generate massive amounts of sensor and communication data. Processing this information solely in centralized cloud servers may introduce unacceptable delays.

My work presented at COMSNETS 2023 investigated hybrid edge-cloud architectures that bring computation closer to vehicles, thereby supporting low-latency applications such as cooperative perception, intelligent navigation, and distributed decision-making.

Building Datasets for Indian Traffic Conditions

One of the major barriers to deploying autonomous systems in India is the lack of representative datasets that capture the complexity of local traffic. Models trained on datasets collected in Europe or North America often fail to generalize to Indian roads characterized by heterogeneous traffic and diverse driving behaviors.

To address this gap, I have contributed to the development of the TiHAN-V2X dataset[6], one of the first large-scale Indian datasets for connected vehicular communication. Such datasets are essential for developing and benchmarking AI algorithms and communication protocols tailored to Indian conditions.

Building an Inclusive AV Ecosystem

AVs could work well for some challenges faced by a megacity. However, technology alone is not sufficient; its development must be inclusive, transparent, and beneficial to society. Through my association with Women in AI, I am committed to promoting broader participation in autonomous mobility research and fostering an ecosystem where there is a transparent understanding of advanced transportation technologies, and socially responsible.

🚦 Conclusion

In this blog, I have described the mobility challenges of a modern, bustling megacity, where AVs can be a part of solutions to expand mobility systems, make them safer, sustainable and more responsive to the needs of the city. 

While technologies such as AVs may be suitable for a range of scenarios, adopting such tech in contexts far removed from where they were originally built , comes with its own set of technological challenges. I have described these as well, and connected it to research in this domain and my own contributions in it - and how that is aligned with social responsibility to the society that this tech will be deployed in. 

References

[1] Telangana Transport Department, Vehicle Statistics. Available: https://transport.telangana.gov.in/html/statistics_vehicles.html.     

[2] TomTom, Traffic Index 2024: Hyderabad Traffic Report.

[3] The Times of India, "Personal vehicles cross 70 lakh mark in Greater Hyderabad," Oct. 2023. (The Times of India)

[4] Greenpeace India / EDGAR Report, "Road transport remains second largest source of NO₂ pollution in Hyderabad," Dec. 2024. (The Siasat Daily)

[5] Down To Earth, "How Hyderabad's urban design has left buses, bicycles and pedestrians behind," May 2025. (Down To Earth)

[6] Annu, V. S. S. Phaneendra, S. V. Srikanth, M. Prasad, and P. Rajalakshmi, "TiHAN-V2X: A Comprehensive Dataset for Dynamic C-V2X Communication within the Indian Context," IEEE DataPort, Oct. 8, 2024. doi: 10.21227/f2kd-9g03.

Acknowledgements

Generative-AI tool use: 

Generative AI tools were used solely for language editing and text refinement. The ideas and perspectives presented in this blog are the author’s own and are informed by the references cited in the text.

Disclaimer

This blog is a contribution of expertise from our volunteers. It is not a reflection of any opinion or roadmap of their employers. All blogs from WAI Labs go through a review and/or editing as needed, and are vetted for veracity. For questions or comments, write to wailabs@womeninai.co.