The EPIC Lab’s research on autonomous vehicle design advances the sensing, perception, security, and system‑level intelligence required for safe, reliable, and energy‑efficient self‑driving platforms. This work spans modality‑aware LiDAR–camera fusion, real‑time and energy‑efficient 3D object detection, and pruning‑based acceleration frameworks that enable robust perception under strict latency and power constraints. Complementary efforts address emerging challenges in software‑defined vehicles, including secure architectures, dependable control, and certification‑ready system design in the era of AI‑driven autonomy. The lab also explores optimal sensor placement, heterogeneous sensing co‑optimization, and perception architectures that remain resilient to environmental variability and cyber‑physical threats. Earlier contributions extend to ADAS technologies and fuel‑efficient driving strategies, forming a continuum from assisted driving to fully autonomous systems. Collectively, this research establishes a comprehensive foundation for next‑generation autonomous vehicles that integrate trustworthy perception, secure and reliable system design, and efficient on‑vehicle intelligence.
Selected Publications
A. Balasubramaniam, S. Pasricha, “MAPLE: Modality-Aware Projection-free LiDAR-Camera Fusion for 3D Vehicular Object Detection”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Verona, Italy, Mar 2026.
B. Ranjbar, K. Raveendiran, S. Pasricha, S. Chakraborty, C. Carbonelli, and A. Kumar, “Autonomous Systems Dependability in the era of AI: Design Challenges in Security, Reliability and Certification”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Verona, Italy, Mar 2026.
A. Balasubramaniam, F. Sunny, S. Pasricha, “UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Mar 2025.
A. El-Fatyany, X. Wang, P. S. Duggirala, S. Chakraborty, S. Pasricha, A. K. Singh, “Emerging Architecture Design, Control, and Security Challenges in Software Defined Vehicles”, IEEE/ACM ESWEEK, Oct 2024.
A. Balasubramaniam, F. Sunny, S. Pasricha, “R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning”, IEEE/ACM DAC, 2023.
V. Kukkala, S. Pasricha, “Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems“, Springer Nature Publishers, 2023.
V. K. Kukkala, S. V. Thiruloga, and S. Pasricha, “Roadmap for Cybersecurity in Autonomous Vehicles”, Vol. 11, Iss. 6, pp. 13-23, IEEE Consumer Electronics, Nov 2022.
J. Dey, S. Pasricha, “Robust Perception Architecture Design for Automotive Cyber-Physical Systems”, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2022
J. Dey, S. Pasricha, “Co-Optimizing Sensing and Deep Machine Learning in Automotive Cyber-Physical Systems”, IEEE Euromicro Conference on Digital Systems Design, 2022
S. V. Thiruloga, V. K. Kukkala, and S. Pasricha, “TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems”, IEEE/ACM Asia & South Pacific Design Automation Conference (ASPDAC), Jan 2022. (Best Paper Award Candidate)
J. Dey, W. Taylor, S. Pasricha, “VESPA: Optimizing Heterogeneous Sensor Placement and Orientation for Autonomous Vehicles”, IEEE Consumer Electronics, 10(2), Mar 2021.
V. K. Kukkala, S. V. Thiruloga, and S. Pasricha, “LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms”, ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 5s, Oct 2021.
V. K. Kukkala, S. V. Thiruloga, and S. Pasricha, “INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, (TCAD), 39(11), Nov 2020
V. Kukkala, J. Tunnell, S. Pasricha, “Advanced Driver Assistance Systems: A Path Toward Autonomous Vehicles“, IEEE Consumer Electronics, Vol. 7, Iss. 5, Sept 2018.
J. Tunnell, Z. Asher, S. Pasricha, T. H. Bradley, “Towards Improving Vehicle Fuel Economy with ADAS”, SAE International Journal of Connected and Automated Vehicles, Oct 2018.
Z. Asher, J. Tunnell, D. A. Baker, R. J. Fitzgerald, F. Banaei-Kashani, S. Pasricha, T. H. Bradley, “Enabling Prediction for Optimal Fuel Economy Vehicle Control,” SAE International, Apr 2018.