Home > Center for Intelligent Mobility (CIM)

Center for Intelligent Mobility (CIM)

  • CIM

Advancing Intelligent Transport

The Center for Intelligent Mobility (CIM) at KLE Tech is a pioneering hub for innovation in autonomous systems, intelligent connectivity, and vehicular cyber-physical security. At the heart of our mission is the creation of safer, smarter, and more sustainable mobility ecosystems.

CIM integrates AI-driven perception and control, secure communication protocols, and rigorous real-world testing to solve next-generation mobility challenges. Whether it’s navigating complex urban traffic, enhancing vehicle-to-vehicle and vehicle-to-infrastructure communication, or ensuring system-level security, our work is grounded in practical impact and scalable design.

Through strategic collaborations with industry leaders like NVIDIA, L&T Technology Services (LTTS), and BOSCH, we ensure that research outcomes go beyond the lab—fueling real-world innovations that shape the future of intelligent transportation.

cim-advance-intelligence-banner
About CIM
  • ABOUT CIM

Smart Moves for a Better World

The Center for Intelligent Mobility (CIM) at KLE Tech is committed to reimagining the future of transportation through innovation in intelligent, connected, and secure mobility systems. Our mission is to develop technologies that make mobility safer, more efficient, and environmentally sustainable.

At CIM, we harness the power of AI, machine learning, sensor fusion, and secure communication frameworks to enable real-time perception, decision-making, and coordination among vehicles in dynamic environments. We place equal emphasis on cybersecurity, ensuring that connected mobility systems are resilient to emerging digital threats.

Driven by a culture of research excellence and industry collaboration, CIM empowers students, researchers, and innovators to lead the development of transformative mobility solutions. Through hands-on projects, cutting-edge tools, and interdisciplinary partnerships, we are shaping the smart transportation systems of tomorrow.

About CIM
  • ABOUT CIM

Smart Moves for a Better World

The Center for Intelligent Mobility (CIM) at KLE Tech aims to redefine mobility through innovation in intelligent systems. Our work focuses on enabling safe, efficient, and sustainable transportation using advances in perception, control, communication, and security. By applying AI/ML models to real-time data, we enhance vehicle awareness, response, and coordination in dynamic environments. Security against cyber threats and seamless vehicle connectivity are central to our mission. Through strategic research and partnerships, we prepare our students and scholars to lead transformative change in mobility technologies.
  • RESEARCHERS AT CIM

The Minds Behind the Motion

s.no
Name
Research Focus
1
Dr. Meena S M
Image Processing, Machine and Deep Learning
2
Dr. B S Anami
Image Processing, Machine and Deep Learning, NLP
3
Dr. V P Baligar
Medical Image Processing, Image Compression
4
Dr. Satyadhyan Chickerur
HPC, CFD, Deep Learning, Quantum Computing
5
Dr. Nirmala
Biomedical Signal & Image Processing
6
Dr. Shivashankar Huddar
CFD, Sensors, AI
7
Dr. Shrinivas Desai
Medical Image Processing, Image Reconstruction, Low Dose Imaging
8
Dr. Suvarna Kanakaraddi
NLP, Medical Image Processing, Data Analytics
9
Dr. Shantala Giraddi
Medical Image Processing, Machine and Deep Learning
10
Dr. Narayan D G
Cloud Computing, SDN, Blockchain
11
Dr. Uday Kulkarni
AI/ML, GenAI, Deep Learning, DNN Optimization
12
Mr. Amit Kachavimath
Cloud Computing, SDN, Network Security
  • FACILITIES AT Cim

Infrastructure That Sees Ahead

CIM offers state-of-the-art facilities to support autonomous systems research and real-world validations
  • Multimodal Data Acquisition:
    Equipped with RADAR, Velodyne LiDAR, Xsense IMU, and other sensors integrated into test vehicles for synchronized data capture.
  • Hardware-in-the-Loop (HIL) Simulation:
    We house the SCALEXIO Processing Unit — real-time processing hardware to simulate, validate, and refine autonomous vehicle algorithms.
  • RESEARCH PROJECTS AT CIM

Vision in Action

CIM’s projects are built on a foundation of research excellence and future-focused innovation. We combine the power of AI, deep learning, and sensor fusion to address real-world mobility challenges—from making autonomous navigation safer to enabling precise spatial understanding. Our work is rooted in practical experimentation and collaboration, pushing the limits of what connected and intelligent transportation can achieve.
  • BEVFlow – Streamlined Excellence in Instance Prediction from a Bird’s-Eye View
  • BEVFlow tackles the challenge of motion prediction in autonomous vehicles using a unique architecture that integrates centripetal motion prediction with backward flow warping. The system uses a multi-view camera input to generate BEV (Bird’s-Eye View) feature maps, which are then processed to produce segmentation maps and future motion flows. A warping-based post-processing module helps reconstruct instance predictions with precision. With a strong focus on accuracy and efficiency, the project involves iterative optimization through hyperparameter tuning and training refinements.
  • Affordance Learning for Detection of Drivable Area
  • This project explores the detection of drivable regions using deep learning-based affordance learning. By training neural networks on multi-modal data from LiDAR, radar, and camera sources, the system maps low-level sensory inputs to high-level affordances such as free space, lane boundaries, and obstacle distances. By fusing spatial and temporal features using advanced convolutional and recurrent models, the system enhances real-time decision-making in dynamic driving environments. Robustness and generalization remain central to its development.
  • ReConfNET – Confidence-Driven SLAM for Scene Reconstruction
  • ReConfNET is a novel RGB-D SLAM framework developed for high-fidelity scene reconstruction in indoor environments. It utilizes a hierarchical generative model and a confidence-based loss function to refine pose estimation and depth prediction. With continuous RGB-D input, it dynamically selects high-confidence key points and uses neural implicit encoding with volume rendering to create scalable reconstructions. The result is a system that outperforms traditional SLAM methods in both accuracy and computational efficiency — with applications across robotics, AR, and intelligent environments.
  • journal

Research That Drives

  • Shet, R.M., Lakhekar, G.V., Iyer, N.C. et al.
  • Robust Path Following Control of AUVs Using Adaptive Super Twisting SOSMC. Journal of Marine Science and Application (2024). DOI
  • Shet, R.M., Lakhekar, G.V. & Iyer, N.C
  • Intelligent fractional-order sliding mode control based maneuvering of an autonomous vehicle. Journal of Ambient Intelligence and Humanized Computing, 15, 2807–2826 (2024). DOI
  • Shet, R.M., Lakhekar, G.V. & Iyer, N.C.
  • Design of quasi fuzzy sliding mode based maneuvering of autonomous vehicle. International Journal of Dynamics and Control, 12, 1963–1986 (2024).DOI
  • Shet, R.M., Lakhekar, G.V. & Iyer, N.C.
  • Design of quasi fuzzy sliding mode based maneuvering of autonomous vehicle. International Journal of Dynamics and Control, 12, 1963–1986 (2024).DOI
  • Shet, R.M., Lakhekar, G.V., Iyer, N.C. et al.
  • Robust Fuzzy Quasi-SMC-Based Steering Control of Autonomous Vehicle Subject to Parametric Uncertainties and Disturbances. International Journal of Automotive Technology(2024). DOI
  • Garagad, V. G., & Iyer, N. C.
  • IoT trust aggregation using hybrid outlier detection and consensus. International Journal of Sensor Networks, 41(4), 229–243 (2023).
  • Kulkarni P., Khanai R., Torse D., Iyer N., Bindagi G.
  • Neural Crypto-Coding Based Approach to Enhance the Security of Images over the Untrusted Cloud Environment. Cryptography, 7(2):23 (2023).DOI
  • Mavinkattimath, S., Khanai, R., Torse, D., & Iyer, N.
  • 
Design and implementation of low-power, high-speed, reliable and secured Hardware Accelerator using 28 nm technology for biomedical devices. Biomedical Signal Processing and Control, 88 (2024): 105554.
  • Wali, Heera G., and Nalini C. Iyer.
  • Power, Performance and Area Analysis of Sponge Based Lightweight HASH Function. International Journal of Electrical and Electronic Engineering & Telecommunications, Vol.12(4): 245–256 (2023).
  • CONFERENCE

Ideas on the move

  • Gireesha, H.M., Shet, R.M., Iyer, N.C., et al. (2024).
  • Perspective transform-based lane detection for lane keep assistance. In: A. Joshi, M. Mahmud, R.G. Ragel, & S. Karthik (Eds.), ICT: Innovation and Computing. ICTCS 2023 (Lecture Notes in Networks and Systems, 879). Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_38
  • Shet, R.M., Iyer, N.C., Mirje, M., Bikkannavar, K.V., & Rokhade, S. (2024).
  • Path planning of autonomous vehicle for real world scenario using CARLA. In: A. Joshi, M. Mahmud, R.G. Ragel, & S. Karthik (Eds.), ICT: Innovation and Computing. ICTCS 2023 (Lecture Notes in Networks and Systems, 879). Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_6
  • Swadi, S., Nissimagoudar, P.C., & Iyer, N.C. (2024).
  • Pixel-level segmentation for multiobject tracking using Mask RCNN-FPN. In: K.K. Patel, K. Santosh, A. Patel, & A. Ghosh (Eds.), Soft Computing and Its Engineering Applications. icSoftComp 2023 (Communications in Computer and Information Science, 2030). Springer, Cham. https://doi.org/10.1007/978-3-031-53731-8_2
  • Sajjan, S., Rudrapur, P., Khushi, H.M., Shriraksha, Katwe, S., & Iyer, N. (2024). 

  • Wheel alignment detection using embedded ML and tyre pressure detection. In: V.K. Gunjan & J.M. Zurada (Eds.), Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. ICMISC 2023 (Lecture Notes in Networks and Systems, 873). Springer, Singapore. https://doi.org/10.1007/978-981-99-9442-7_46
  • Purohit, A., Shet, R.M., Iyer, N.C., & Nissimagoudar, P.C. (2024).
  • Comparative study of PID and MPC controller on four wheel vehicle. In: A. Abraham, A. Bajaj, T. Hanne, P. Siarry, & K. Ma (Eds.), Intelligent Systems Design and Applications. ISDA 2023 (Lecture Notes in Networks and Systems, 1051). Springer, Cham. https://doi.org/10.1007/978-3-031-64850-2_35
  • Nissimagoudar, P., et al. (2024).
  • Performance analysis of anti-lock braking system (ABS) for different road surfaces. In: A. Abraham, A. Bajaj, T. Hanne, P. Siarry, & K. Ma (Eds.), Intelligent Systems Design and Applications. ISDA 2023 (Lecture Notes in Networks and Systems, 1050). Springer, Cham. https://doi.org/10.1007/978-3-031-64847-2_42
  • Ankad, S., Patil, V., Bhat, S., Channalli, S., Basawaraj, & Nissimagoudar, P.C. (2024). Sensor interfacing with multicore Aurix TC397 board. In J. Choudrie, P.N. Mahalle, T. Perumal, & A. Joshi (Eds.), ICT for Intelligent Systems. ICTIS 2024(Lecture Notes in Networks and Systems, 1110). Springer, Singapore. https://doi.org/10.1007/978-981-97-6678-9_5
  • Sanmani, V., Bennadi, P., Maharajpet, S., Nissimagoudar, P., Mallibhat, K., & Iyer, N. (2024, May). 
3D object detection using semantic maps. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1–6). IEEE.
  • Purohit, A., Shet, R.M., Iyer, N.C., & Nissimagoudar, P.C. (2024).
  • Comparative study of PID and MPC controller on four wheel vehicle. In: A. Abraham, A. Bajaj, T. Hanne, P. Siarry, & K. Ma (Eds.), Intelligent Systems Design and Applications. ISDA 2023 (Lecture Notes in Networks and Systems, 1051). Springer, Cham. https://doi.org/10.1007/978-3-031-64850-2_35
  • Nissimagoudar, P., Basawaraj, Gireesha, H.M., Kulkarni, A., Bhat, S., & Iyer, N.C. (2023). Pothole detection and road condition updation on Google Maps. In J. Choudrie, P.N. Mahalle, T. Perumal, & A. Joshi (Eds.), IOT with Smart Systems. ICTIS 2023 (Lecture Notes in Networks and Systems, 720). Springer, Singapore. https://doi.org/10.1007/978-981-99-3761-5_5
  • Desai, C., et al. (2023). LightNet:
  • Generative model for enhancement of low-light images. In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France (pp. 2223–2232). IEEE. https://doi.org/10.1109/ICCVW60793.2023.00237
  • Jain, M., Kamat, G., Bachari, R., Belludi, V.A., Hegde, D., & Patil, U. (2023, December). AfforDrive: Detection of drivable area for autonomous vehicles. In International Conference on Pattern Recognition and Machine Intelligence (pp. 532–539). Springer, Cham.
  • Pillai, P.S., Kinnal, B., Pattanashetty, V., & Iyer, N.C. (2023).
  • Traffic sign detection and recognition. In A. Joshi, M. Mahmud, & R.G. Ragel (Eds.), Information and Communication Technology for Competitive Strategies (ICTCS 2021)(Lecture Notes in Networks and Systems, 400). Springer, Singapore. https://doi.org/10.1007/978-981-19-0095-2_18
  • Morabad, D., Nissimagoudar, P., Gireesha, H.M., & Iyer, N.C. (2023)
  • Multi-level association-based 3D multiple-object tracking framework for self-driving cars. In: J. Choudrie, P.N. Mahalle, T. Perumal, & A. Joshi (Eds.), ICT with Intelligent Applications. ICTIS 2023 (Lecture Notes in Networks and Systems, 719). Springer, Singapore. https://doi.org/10.1007/978-981-99-3758-5_32
  • Wali, H., Shraddha, B.H., & Iyer, N.C. (2023).
  • Authenticated encryption engine for IoT application. In: A. Abraham, T.P. Hong, K. Kotecha, K. Ma, P. Manghirmalani Mishra, & N. Gandhi (Eds.), Hybrid Intelligent Systems. HIS 2022 (Lecture Notes in Networks and Systems, 647). Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_101
  • Nissimagoudar, P., Gireesha, H.M., Korlahalli, C.L., Nandi, A.V., & Iyer, N.C. (2023). EEG-based driver performance estimation using deep learning. In: S. Fong, N. Dey, & A. Joshi (Eds.), ICT Analysis and Applications. ICT4SD 2023(Lecture Notes in Networks and Systems, 782). Springer, Singapore. https://doi.org/10.1007/978-981-99-6568-7_35
  • Shet, R.M., & Iyer, N.C. (2023).
  • Comparative study of trajectory tracking control for autonomous vehicles via geometric and model-based method. AIP Conference Proceedings, 2901(1), 080006. https://doi.org/10.1063/5.0178664
  • Nissimagoudar, P., Algur, N., Bonageri, N., Chavan, A., Koppa, A., & Iyer, N.C. (2023). Multiple vehicle tracking using Meanshift algorithm and 8-point connectivity. In A. Abraham, T. Hanne, N. Gandhi, P. Manghirmalani Mishra, A. Bajaj, & P. Siarry (Eds.), Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) (Lecture Notes in Networks and Systems, 648). Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_1
  • Nissimagoudar, P.C., Iyer, N.C., Gireesha, H.M., Pillai, P., & Mallapur, S. (2022). Multi-pedestrian tracking and person re-identification. In A. Abraham et al. (Eds.), Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (Lecture Notes in Networks and Systems, 417). Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_16
  • Nikita, P., Shraddha, B.H., Mane, V., Shashidhar, N., Vishal, P., & Iyer, N.C. (2023).
  • System design and implementation of assistive device for hearing impaired people. In Y.D. Zhang, T. Senjyu, C. So-In, & A. Joshi (Eds.), Smart Trends in Computing and Communications (Lecture Notes in Networks and Systems, 396). Springer, Singapore. https://doi.org/10.1007/978-981-16-9967-2_53
  • Mane, V., Kubasadgoudar, A.R., Shet, R., & Iyer, N.C. (2023).
  • An IoT-based smart parking system for smart cities. In Y.D. Zhang, T. Senjyu, C. So-In, & A. Joshi (Eds.), Smart Trends in Computing and Communications (Lecture Notes in Networks and Systems, 396). Springer, Singapore. https://doi.org/10.1007/978-981-16-9967-2_51
  • Mane, V., Kubasadgoudar, A.R., Nikita, P., & Iyer, N.C. (2023).
  • RADAR and camera sensor data fusion. In A. Joshi, M. Mahmud, & R.G. Ragel (Eds.), Information and Communication Technology for Competitive Strategies (ICTCS 2021)(Lecture Notes in Networks and Systems, 400). Springer, Singapore. https://doi.org/10.1007/978-981-19-0095-2_75
  • Mane, V., Vishal, P., Nikita, P., Iyer, N.C., & Ashwini, K. (2023).
  • Model-based design technique to monitor vehicle exhaust emissions. In M.S. Kaiser, J. Xie, & V.S. Rathore (Eds.), Information and Communication Technology for Competitive Strategies (ICTCS 2021) (Lecture Notes in Networks and Systems, 401). Springer, Singapore. https://doi.org/10.1007/978-981-19-0098-3_75
  • Iyer, N.C., Nissimagoudar, P.C., Pillai, P., Gireesha, H.M., Kulkarni, A., & Okade, A. (2022). Perception of autonomous vehicle for localization using camera and GPS. In A. Abraham et al. (Eds.), Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (Lecture Notes in Networks and Systems, 417). Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_8
  • Shraddha, B.H., Kinnal, B., Wali, H., Iyer, N.C., & Vishal, P. (2022).
  • Lightweight cryptography for resource-constrained devices. In A. Abraham et al. (Eds.), Hybrid Intelligent Systems. HIS 2021 (Lecture Notes in Networks and Systems, 420). Springer, Cham. https://doi.org/10.1007/978-3-030-96305-7_51
  • Heera G Wali, Nalini C Iyer, and Shraddha B Hiremath.  (2023).
  • Hardware implementation of SPONGENT lightweight hash algorithm. In Proceedings of the 2022 12th International Conference on Communication and Network Security (ICCNS '22) (pp. 105–109). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3586102.3586118
  • Pillai, P.S., Shet, R., Iyer, N.C., & Punagin, S. (2022).
  • Modeling and simulation of an automotive RADAR. In A. Joshi, M. Mahmud, R.G. Ragel, & N.V. Thakur (Eds.), Information and Communication Technology for Competitive Strategies (ICTCS 2020) (Lecture Notes in Networks and Systems, 191). Springer, Singapore. https://doi.org/10.1007/978-981-16-0739-4_32
  • RESEARCH FOCUS AREAS

Domains of Impact

01

Perception and Control

02

Security

03

Connectivity