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The FISITA Intelligent Safety Conference China was established in 2019 by FISITA and China SAE. The conference brings together global scholars, technical leaders and engineers to discuss the latest innovative methods, technologies and solutions in the field of intelligent driving safety.
The FISITA Intelligent Safety Conference China will deliver a high-quality, national and international speaker line-up, which will consider and discuss the important topics of the strategy, the technology roadmap, and key technologies for intelligent safety.
The 2025 FISITA Intelligent Safety Conference(ISC 2025) will take place on 13-14 September 2025, at Xixian Conference Center, Xi'an, China.
The topics to be discussed include:
· Al & Security
· SOTIF
· Connected Cooperative Driving
· Test & Evaluation
· Information security
· Ship & ShippingIntelligent Safety
In addition to these technical sessions, we will be giving early-career engineers the opportunity to take part in an online Global Young Scientist Forum where they will be discussing "New in AI and Autonomous Driving Safety—Emerging Problems, Technologies and Practices" and the challenges when implementing advanced artificial intelligence into autonomous driving.
Conference chairs:
Jun Li, Academician of China Engineering Academy, President of China SAE, Professor at Tsinghua University, Editor-in-Chief of Automotive Innovation
Frank Zhao, Honorable Lifetime President of FISITA, Director of Tsinghua Automotive Strategy Research Institute, Professor at Tsinghua University, Editor-in-Chief of Automotive Innovation
Chris Mason, FISITA CEO
Language: English, simultaneous interpretation will be available.
Session Chairs:
Xianyuan Zhan, Associate Professor of Intelligent Industry Research Institute (AIR) at Tsinghua University
Siyuan Gong, Deputy Director and Professor of the Department of Computer Science at the School of Information Engineering, Chang'an University
TBD
Towards Creating Safe and Generalizable Autonomous Driving
In this talk, we will walk through the most recent advances towards creating safer and more generalizable autonomous systems. In particular, we will focus on an End-2-End flywheel to jointly develop safer models and improve the quality of the datasets used to train those models.
TBD
Autonomous Driving Decision-Making and Planning with Generative Models
Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning and decision-making approaches often struggle to balance competing objectives and heavily rely on fallback strategies with predefined rules, primarily due to their limited adaptability and inadequacy in learning complex multi-modal human driving behaviors. The recently emerged generative AI models possess great potential to address these challenges. Their ability to model complex, multi-modal human driving behavior provides a new opportunity for generating high-quality planning trajectories or control strategies without relying on rule-based refinement. In this talk, we will discuss how cutting-edge generative models (such as diffusion and flow matching models) can be used to build strong planning or control models for autonomous driving, as well as their real-world deployments.
Unveiling Safety Hysteresis in Traffic Dynamics
Hysteresis has been extensively studied in traffic flow analysis and remains a prominent topic of research. However, its implications for traffic safety have received limited attention. This study aims to bridge this gap by investigating the presence and characteristics of hysteresis in traffic safety using real-world vehicle trajectory data and simulation experiments. The inverse Time-to-Collision is employed as the safety indicator to identify and analyze patterns of safety hysteresis. Empirical analysis reveals distinct hysteresis loops in stop-and-go traffic. To quantify the extent of this phenomenon, a new metric, termed safety Hysteresis Intensity (HI), is proposed. Simulation results demonstrate that increased deceleration significantly amplifies HI, whereas acceleration has a comparatively minor effect.
AI for End-to-End Autonomous Driving
World Model, Ushering into a new Era of Physical AGI
Session Chair:
Hong Wang, Associate Research Professor, School of Vehicle and Mobility, Tsinghua University; Executive Director, CAICV-SOTIF Technical Alliance
Statistical Validation of SAE Level 3 Highway Driving
BMW has developed SIFAD—the Safety Integrity Framework for Automated Driving—to enable the safe launch of one of the world’s first SAE Level 3 Automated Highway Driving Systems. This talk focuses on one of its most critical parts: statistical validation. It will show how Bayesian analysis, designed experiments, and large-scale field data were used to quantify uncertainties, simulate residual risks, and ensure a Positive Risk Balance—integrating these methods seamlessly into the V-Model for safety-critical automotive systems.
A Practical Approach for the Implementation of SOTIF Requirements in ADS and ADAS Development
With the spread of automated driving, the importance of SOTIF is becoming increasingly apparent, too. Current regulations and standards do not provide developers with sufficient support in identifying and avoiding SOTIF hazards. This lesson offers a practical approach for integrating SOTIF into a comprehensive safety lifecycle. As an example, a comprehensive HARA – considering SOTIF and functional safety – was carried out for a Level 4 ADS for driverless vehicles. The result is an ASIL D-capable, fail-operational E/E architecture. Although the current regulations for Level 2 do not include SOTIF requirements, the approach can be used for the future development and approval of ADAS. Considering the findings of recent accidents, requirements arise, e.g., regarding
Avoidance of wrong expectations on the part of users
Effective monitoring of user attention
Timely and appropriate intervention of technical systems and
Clear HMI presentation of capabilities and limitations of the systems and required user interventions.
Autonomous Driving Test Scenarios Complexity Grading Using Primitive Analysis
Vehicle-in-the-Loop Testing of Highly Automated Driving Functions including Automotive Perception Sensors
Purpose
Highly-automated driving functions must be proven safe before market release, but billions of real-world test-kilometres are impractical and dangerous. The work therefore aims to replace much of this open-road testing with realistic, reproducible and safe laboratory tests that still capture the effects of adverse weather on perception sensors.
Method
• A unique outdoor “rain plant” produced controlled rainfall (up to 100 mm h⁻¹) while vehicles performed critical traffic manoeuvres.
• Reference data were gathered with a 128-beam LiDAR, GPS/IMU and ground-truth object lists.
• Sensor-detection errors under rain and varying illumination were modelled by data-driven Mixture-Density-Networks that balance fidelity with real-time capability.
• The resulting camera-, radar- and LiDAR-models were integrated into a full-vehicle Vehicle-in-the-Loop (ViL) testbed running Apollo open-source AD software; real vehicle actuators were driven by chassis dynamometers while the virtual environment and sensor feeds ran in Simulink/CarMaker at real-time speed.
Key findings
• The MDN-based sensor models reduce simulation-to-reality error by ≈32 % compared with commercial equivalents.
• 253 critical traffic cases (extracted from daily driving and the HighD data set) were successfully reproduced in the ViL rig; Apollo safely performed an evasive lane-change with the same 1.8 s TTC that a human driver required.
• The approach provides safe, efficient and repeatable testing of highly-automated driving functions under adverse-weather conditions without exposing prototypes to public roads.
TBD
Autonomous Driving Risk Cognition and Control
Session Chairs:
Jianqiang Wang, Professor and the Dean, School of Vehicle and Mobility, Tsinghua University
Yafei Wang, Professor, Shanghai Jiao Tong University
Research on Safety Integration Technology for Intelligent Connected Vehicles
TBD (under invitation)
CAV-Based Optimal Path Planning and AWD Torque Vectoring for Energy-Efficient and Stable Driving Control
Distributed Consensus and Collaboration for Connected Autonomous Vehicles
Vehicle-Infrastructure Cooperative Perception and Prediction: Challenges and Solutions
Sustainable Transport Future Using Electrified Roads and Shared Autonomous Vehicles
Opening Ceremony
Research on ADS Driving Safety Evaluation Method Based on the SOTIF Index
Security & Safety Issues and Endogenous Security for Intelligent Connected Vehicles
GenAI-Based Secured Communications for Low-Altitude Economy Netwo
Road Traffic Testing Technology System Innovation and Engineering Practice
Insights into Intelligent Safety Research and Practice Based on Immunology
TBD
Toyota’s Approach to Safety
TBD
Insights into the Implementation of Autonomous Driving Safety & Security
Panel Discussion: Development Trends and Safety Challenges of Autonomous Driving Technology
Chairman:
Professor Zhang Xinjie from Jilin University, Deputy Director of the National Key Laboratory of Automotive Chassis Integration and Bionics, and Executive Deputy Editor in Chief of Automotive Innovation
Feng Shuo, Associate Professor at the School of Automation, Tsinghua University
Host guest:
Feng Shuo, Associate Professor at the School of Automation, Tsinghua University
Tariq Javaid, Founder and Director of ECOSAR Technology Compan
TBD
High-fidelity Simulation and Behavioral Safety Assessment of Autonomous Vehicles
Evolution of Autonomous Driving Simulation: Adapting to the Latest Industry Tech Trends
Traffic World Modeling Accelerates Unbiased Safety Evaluation of Autonomous Vehicles
Virtual Testing Tool VTS for End-to-end Autonomous Driving
A Shift in Focus: Why MiL and SiL are as Critical (if not more) as HiL for System V&V
Chairman:
Yang Shichun, Dean and Professor of the School of Transportation Science and Engineering, Beihang University, Beijing
Muttukrishnan Rajarajan Professor, University of London
Host guest:
Muttukrishnan Rajarajan Professor, University of London
Cao Yaoguang, Associate Professor at Beihang University, Beijing
Cyber Threats Analysis of Intelligent Connected Vehicles: From Sensing to Networking
Continuous Device-to-Device Authentication in Autonomous Vehiles
Every day, on average, eight cybercrimes targeting Internet-of-Things (IoT) networks occur, resulting in a cumulative financial loss of approximately $10 million. One of the primary reasons behind these attacks is the ability of unauthorised devices to infiltrate IoT networks by mimicking the hardware and software configurations of legitimate devices. Most IoT networks employ cryptographic keys for device authentication to mitigate this risk. However, authentication is typically performed only once during the device onboarding/installation stage. Once authenticated, devices remain trusted indefinitely, leaving the devices vulnerable to large scale future cyber-attacks which can occur long after the initial authentication.
To address this critical gap, we propose a novel framework called Continuous Device-to-Device Authentication (CD2A), designed to enhance Internet of Things (IoT) security through continuous device identity verification. The CD2A framework comprises two core components: Identity Establishment and Continuous Authentication.
Vulnerability Challenges and Endogenous Security in Intelligent Connected Vehicles (ICVs)
The rise of software-defined vehicles (SDVs) has exponentially expanded attack surfaces in intelligent connected vehicles (ICVs), amplifying inherent software vulnerabilities. Addressing these vulnerabilities
requires a dual focus : detection and defense. Effective detection mandates granular component-level scrutiny, as systemic vulnerabilities in any subsystem (e.g., perception, control, or actuation) compromise overall vehicle security. Traditional defense paradigms, reliant on static signatures or rule-based detection, struggle against zero-day vulnerabilities exploits and sophisticated adversarial attacks. To tackle this, we propose Endogenous Security, a novel framework integrating dynamic，eterogeneity, redundancy for achieving structural encryption of systems. This approach embeds security "genes" into
execution environments, enabling self-immune responses to both known and unknown threats while enabling quantifiable design and measurable verification. Notable achievements have been realized in
deploying and validating endogenous security, particularly in smart connected vehicles (SCVs).
Automotive Cybersecurity and Resilience: What does the future hold?
Interpretation of Security Policies and Testing and Evaluation Practices for Intelligent and Connected Vehicles
Cybersecurity Risks for SDVs - How to Address Them
New use cases using advanced software and connectivity continue to emerge for SDVs. These use cases also introduce cybersecurity risks. In this presentation, we will discuss these risks and what the automotive industry can do to address them. We will introduce both technical and organizational solution approaches.
TBD
TBD
Jianru Xue earned his PhD from Xi'an Jiaotong University in 2003. From 2002 to 2003, he conducted collaborative research at Fuji Xerox Co., Ltd. in Japan. From 2008 to 2009, he was a visiting scholar at the University of California, Los Angeles. He is currently a professor and doctoral advisor at Xi'an Jiaotong University. His primary research areas include computer vision and pattern recognition, machine learning, and autonomous intelligent systems. He has co-authored one English-language monograph and published over 100 papers in domestic and international journals and conferences. He has received one National Natural Science Award Second Prize and one National Invention Award Second Prize. He has been selected as a National Leading Talent in Science and Technology Innovation and an Outstanding Young Talent of the Ministry of Education. He has also received the IEEE Intelligent Transportation Society Outstanding Research Team Award, the Chinese Automation Society Young Scientist Award, the Shaanxi Province Young Science and Technology Award, and the Shaanxi Province Young Pioneer Award. He currently serves as the Chair of the Hybrid Intelligence Professional Committee of the Chinese Automation Society, a Council Member of the China Society of Graphics and Imaging, and the Vice Chair of the Visual Big Data Professional Committee.
Towards Creating Safe and Generalizable Autonomous Driving
Jose Alvarez is Director of Research at NVIDIA, where he leads the Autonomous Driving Applied Research Team. His work focuses on scaling deep learning and advancing its application to safe and reliable autonomous vehicles.
Prior to joining NVIDIA, Dr. Alvarez held research positions at the Toyota Research Institute and NICTA (Australia). He earned his Ph.D. in Barcelona under the supervision of Prof. Antonio López and Prof. Theo Gevers, and later completed a postdoctoral fellowship at New York University with Prof. Yann LeCun.
Dr. Alvarez is a recipient of multiple awards in the field of autonomous driving and has published over 100 papers in top-tier machine learning and computer vision venues, accumulating more than 22,000 citations. He regularly serves as an Area Chair for leading conferences such as NeurIPS, ECCV, KDD, and ICRA, led the organization of several prominent workshop series, including DeepVision and the Workshop on Autonomous Driving; and sits on the editorial boards of IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Pattern Analysis and Machine Intelligence.
TBD
Hafiz Malik is Professor of Electrical and Computer Engineering (ECE) at the University of Michigan – Dearborn. His research in the areas of automotive cybersecurity, deepfakes, IoT security, sensor security, multimedia forensics, steganography/steganalysis, information hiding, pattern recognition, and information fusion is funded by the National Science Foundation, National Academies, Ford Motor Company, Marelli, Inc. N.A., and other agencies. He is a recipient of the UM – Dearborn 2022 Distinguished Research Award and College of Engineering and Computer Science 2020 Excellence in Research Award. He has published over 150 articles in leading peer-reviewed journals, conferences, and workshops. He is a founding member and chief operating officer (COO) of the Global Foundation for Cyber Studies and Research, a founding member of the Cybersecurity Center for Research, Education, and Outreach at UM-Dearborn and member of leadership circle for the Dearborn Artificial Intelligence Research Center at UM-Dearborn. He is also a member of the Scientific and Industrial Advisory Board (SIAB) of the National Center of Cyber Security Pakistan. He is a member of MCity Working Group on Cybersecurity, since 2015.
Autonomous Driving Decision-Making and Planning with Generative Models
Dr. Xianyuan Zhan is a research associate professor at the Institute for AI Industry Research (AIR), Tsinghua University, with a joint appointment as a Young Researcher at the Shanghai Artificial Intelligence Laboratory. Before joining AIR, Dr. Zhan was a data scientist at JD Technology and also a researcher at Microsoft Research Asia (MSRA). His research directions include data-driven decision-making, embodied AI, and autonomous driving. He has published more than 70 papers in key conferences and journals in the field of Computer Science and Transportation Engineering, and 20 granted patents. He is also a committee member of Artificial Intelligence & Pattern Recognition Committee (CCF-AI) and Intelligent Vehicles Committee (CCF-IV) of China Computer Federation (CCF). He was also selected as one of the Top 50 Global AI Young Chinese Scholars (in the field of AI+X) by Baidu in 2022.
Unveiling Safety Hysteresis in Traffic Dynamics
Dr. Zhu, Feng is an Associate Professor in the School of Civil and Environmental Engineering at Nanyang Technological University (NTU), Singapore. He received his Ph.D. degree from Purdue University in 2016, an M.Phil. degree from the Hong Kong University of Science and Technology in 2011, and a B.E. degree from Sun Yat-sen University in 2009. Dr. Zhu’s research focuses on urban mobility, connected and automated vehicles, traffic safety, and data analytics. His work has been published in leading SCI journals in transportation research. His research has received funding from a range of agencies and institutions, such as Singapore’s Ministry of Education, the Land Transport Authority, the Urban Redevelopment Authority, the National Environment Agency, and the Surbana Jurong–NTU Corporate Lab.
AI for End-to-End Autonomous Driving
World Model, Ushering into a new Era of Physical AGI
Statistical Validation of SAE Level 3 Highway Driving
Moritz Werling is a Principal Engineer at BMW with over 20 years of experience in automated driving. During his doctoral studies at the Karlsruhe Institute of Technology (KIT), he focused on motion planning and control and spent part of his PhD as a visiting researcher at Stanford University. The results of his research have been implemented in numerous production applications. Since 2011, he has been working as a developer for automated driving at BMW AG. He has also been a lecturer on automated driving at KIT since 2015 and at the Technical University of Munich (TUM) since 2024. Since 2019, his work has focused on developing processes and methods for the design, verification, and validation of safe automated driving systems.
A Practical Approach for the Implementation of SOTIF Requirements in ADS and ADAS Development
I graduated with a diploma in physics from Technical University of Dresden in 1986. For the next five years, I worked in nuclear safety research at Energiewerke Nord AG in Berlin. In 1991, I joined TÜV SÜD in Munich, where I worked in the field of risk, reliability and safety of human-machine systems in the industrial, railway and automotive sectors, recently as Chief Expert Automotive Safety. I’m engaged in the safety of automated driving since 2005.
I participated in the national standards committee for ISO 26262 and ISO 21448 and in national legislation on automated and autonomous driving. I am a guest lecturer at Ingolstadt University of Applied Sciences and at various private educational institutions.
In 2024, I co-founded and became managing director of TESACO GmbH. We advise on the development, approval and operation of vehicles and systems for Connected Cooperative Automated Mobility.
Autonomous Driving Test Scenarios Complexity Grading Using Primitive Analysis
Vehicle-in-the-Loop Testing of Highly Automated Driving Functions including Automotive Perception Sensors
Born 1969, he studied Mechanical Engineering at the University of Technology Graz, where he graduated 1995. He received a doctor’s degree in technical sciences in 1998 with distinction.
From 1998 to 2007 Arno Eichberger was employed at MAGNA STEYR Fahrzeugtechnik AG&Co and dealt with different aspects of active and passive safety.
Since 2007, Arno Eichberger is employed at the Institute of Automotive Engineering (University of Technology Graz) as vice-director of the Institute and head of the research area Vehicle Dynamics. His researches include development and testing of automated driving, human-machine interaction, vehicle dynamics control and suspension development.
Since 2012 he is Associate Professor holding a venia docendi in Automotive Engineering.
TBD
Autonomous Driving Risk Cognition and Control
Research on Safety Integration Technology for Intelligent Connected Vehicles
TBD (under invitation)
CAV-Based Optimal Path Planning and AWD Torque Vectoring for Energy-Efficient and Stable Driving Control
Distributed Consensus and Collaboration for Connected Autonomous Vehicles
Vehicle-Infrastructure Cooperative Perception and Prediction: Challenges and Solutions
Sustainable Transport Future Using Electrified Roads and Shared Autonomous Vehicles
Opening Ceremony
Keynote Speech
Research on ADS Driving Safety Evaluation Method Based on the SOTIF Index
Prof. Jun Li is currently an academician of the Chinese Academy of Engineering, professor at School of Vehicle and Mobility with Tsinghua University, president of the China Society of Automotive Engineers, director of the Expert Committee of China Industry Innovation Alliance for the Intelligent and Connected Vehicles. He has been chief engineer and director of technology center in China FAW Group. His research interests include internal combustion engine, electric drive systems, electric vehicles, and intelligent connected vehicles.
Research experience：
Prof. Jun Li has long presided over the product R&D and technological innovation for major automobile companies in China. He established a leading independent research system and completed a number of major product replacement projects, major national projects, and major equipment model development projects. In the field of automotive powertrain, he presided over the development of heavy-duty series diesel engines, series products of direct injection supercharged gasoline engines, heavy-duty equipped trucks, military vehicles, and HongQi luxury cars. He also presided over the R&D and mass produce of the diesel automated electronic control system products. Further, he invented dual-track electronically controlled variable fuel injection regular high-pressure systems and gas-driven urea injection system. For new energy vehicle, he invented a strong hybrid configuration with dual coupling of motor, engine, and gearbox, and presided over the development of the first full hybrid and plug-in hybrid vehicles as well as hybrid bus products. In the area of intelligent connected vehicles, he proposed a technology strategy, "Zhitu", and technical route for China FAW’s intelligent connected vehicles. In addition, he designed the HongQi L3 prototype vehicle and the forward-looking R&D of smart city intelligent automobiles.
Security & Safety Issues and Endogenous Security for Intelligent Connected Vehicles
GenAI-Based Secured Communications for Low-Altitude Economy Netwo
Road Traffic Testing Technology System Innovation and Engineering Practice
Xiangmo Zhao received the B.S. degree from Chongqing University, Chongqing, China, in 1987, and the M.S. and Ph.D. degrees from Chang'an University, Xi'an, China, in 2002 and 2005, respectively. He is currently a Professor with the School of Information Engineering, Chang'an University, and the President of Xi'an Technological University, Xi'an. He is a Vice President of the Joint Laboratory for Connected vehicles, Ministry of Education and China Mobile, and the Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Centre, and is also a Leader of the National Key Subjects-Traffic Information Engineering and Control, Chang'an University. His research interests include connected vehicles, automated vehicles, intelligent transportation systems, and computer science. He is the Director of the Information Professional Committee, a member of the Advisory Expert Group of the China Transportation Association, member of the National Motor Vehicle Operation Safety Testing Equipment Standardization Committee and the Leading Group of the National Traffic Computer Application Network, the Vice Chairperson of the Institute of Highway Association on Computer Professional Committee, and the Deputy Director of the Institute of Computer in Shaanxi Province.
Insights into Intelligent Safety Research and Practice Based on Immunology
TBD
Toyota’s Approach to Safety
TBD
Insights into the Implementation of Autonomous Driving Safety & Security
Panel Discussion: Development Trends and Safety Challenges of Autonomous Driving Technology
TBD
High-fidelity Simulation and Behavioral Safety Assessment of Autonomous Vehicles
Evolution of Autonomous Driving Simulation: Adapting to the Latest Industry Tech Trends
Traffic World Modeling Accelerates Unbiased Safety Evaluation of Autonomous Vehicles
Virtual Testing Tool VTS for End-to-end Autonomous Driving
A Shift in Focus: Why MiL and SiL are as Critical (if not more) as HiL for System V&V
Cyber Threats Analysis of Intelligent Connected Vehicles: From Sensing to Networking
Continuous Device-to-Device Authentication in Autonomous Vehiles
Raj received his BEng and PhD degrees from City University of London in 1994 and 1999 respectively. From 1999 he worked at City University of London as a Research Fellow. In August 2000 he moved to CMG as a Telecommunications Consultant. After a few years in the industry Raj is now a Professor of Security Engineering. He is currently the Director of the Institute for Cyber Security at City University of London and carries out research in the areas of privacy preserving data management, Internet of Things privacy, network intrusion detection, cloud security and identity management using blockchain. Raj has received funding from EPSRC, Royal Academy of Engineering, European Commission, Innovate UK, British Council and industry to carry out research in cyber security. He has supervised several PhDs jointly with British Telecommunications, UK in the area if data analytics for cyber security and network intrusion detection. To date Raj has graduated more than 30 PhD students in the area of Cyber Security and continues to supervise a good number of students in the area of Cloud Security, Privacy and blockchain.
He is also the Programme Director for the MSc in Project Management, Finance and Risk.
He is a senior member of IEEE, a member of IET and an associate member of the charted institute of information security and a member of Technical Programme Committees of several security and privacy conferences.
Raj is the CEO of CityDefend (www.citydefend.com) a start up in the area of secure search in the encrypted Cloud. He also sits on the Editorial boards of the Journal on Wireless Networks, Elsevier Journal of Health Policy and Technology and Emerald Journal of Information Management and Computer Security.
Vulnerability Challenges and Endogenous Security in Intelligent Connected Vehicles (ICVs)
Yufeng Li, Professor and PhD Supervisor at Shanghai University, serves as the head of the network space security program at Shanghai University. He is the academic leader in Intelligent Connected Vehicle (ICV) network security at Purple Mountain Laboratory and Songshan Laboratory, and the director of the Shanghai ICV Network Security Industrial Collaborative Innovation Center. Additionally, he holds the position of Executive Director of the Shanghai Key Laboratory of ICV Network Security. He is recognized as an expert in the National Key Special Project on New Energy Vehicles and serves as the deputy director of the ICV Safety Committee under the China Automotive Engineering Society, as well as the deputy director of the Artificial Intelligence Committee of the China Network Space Endogenous Security Alliance.
Yufeng Li has long dedicated his research to vehicle networking security and ICV security . His notable achievements include authoring 3 monographs, publishing over 100 papers, and securing 42 national invention patents. He has received 4 provincial or ministerial-level technology invention and scientific progress first prizes and 2 second prizes. His work focuses on advancing cybersecurity frameworks for smart vehicles and networked transportation systems.
Automotive Cybersecurity and Resilience: What does the future hold?
Siraj Shaikh is a Professor in Systems Security at Swansea University, where his research interests sit at the interface of automotive cybersecurity, systems engineering and cyber-physical systems security. He is the Chair of the Research and Innovation SIG at UK’s Automotive Electronics Systems Innovation Network (AESIN) and also the Safety and Security Workstream of the Global Automotive Advisory Council at SEMI (the leading semiconductor industry body). Siraj is a Certified Automotive Cyber-Security Professional, SGS-TÜV (2022), relating to ISO/SAE 21434 standard: extensive knowledge of its content; extensive experience of the interpretation of its requirements, its recommendations and its permissions; and extensive technical understanding of the methodology to implement its requirements. Siraj is well published in automotive cybersecurity, and he currently is a Member of the College of Experts part of the UK’s Department for Transport (DfT) Scientific Advisory Council (SAC). Previously, he has been an Industrial Fellow to HORIBA MIRA, funded by the Royal Academy of Engineering (RAEng). More recently he served as an Independent Scientific Adviser to The Alan Turing Institute under the BridgeAI initiative. He has also served as a RITICS Fellow on Economics of CNI Security at Imperial College (UK).
Interpretation of Security Policies and Testing and Evaluation Practices for Intelligent and Connected Vehicles
Cybersecurity Risks for SDVs - How to Address Them
Dr. Dennis Kengo Oka is an automotive cybersecurity expert with nearly 20 years of global experience in the automotive industry. He received his Ph.D. in automotive security focusing on solutions for the connected car. As Global Technical & Cybersecurity Advisor at IAV, he focuses on cybersecurity solutions for software-defined vehicles, both on-road and off-road vehicles. Dennis has over 80 publications consisting of conference papers, journal articles and books, and is a frequent public speaker at international automotive and cybersecurity conferences and events. His latest published books are "Building Secure Cars: Assuring the Automotive Software Development Lifecycle" (Wiley, 2021), and "Building Secure Automotive IoT Applications: Developing Robust IoT Solutions for Next-Gen Automotive Software" (Packt, 2024).
Prof. Jun Li is currently an academician of the Chinese Academy of Engineering, professor at School of Vehicle and Mobility with Tsinghua University, president of the China Society of Automotive Engineers, director of the Expert Committee of China Industry Innovation Alliance for the Intelligent and Connected Vehicles. He has been chief engineer and director of technology center in China FAW Group. His research interests include internal combustion engine, electric drive systems, electric vehicles, and intelligent connected vehicles.
Research experience：
Prof. Jun Li has long presided over the product R&D and technological innovation for major automobile companies in China. He established a leading independent research system and completed a number of major product replacement projects, major national projects, and major equipment model development projects. In the field of automotive powertrain, he presided over the development of heavy-duty series diesel engines, series products of direct injection supercharged gasoline engines, heavy-duty equipped trucks, military vehicles, and HongQi luxury cars. He also presided over the R&D and mass produce of the diesel automated electronic control system products. Further, he invented dual-track electronically controlled variable fuel injection regular high-pressure systems and gas-driven urea injection system. For new energy vehicle, he invented a strong hybrid configuration with dual coupling of motor, engine, and gearbox, and presided over the development of the first full hybrid and plug-in hybrid vehicles as well as hybrid bus products. In the area of intelligent connected vehicles, he proposed a technology strategy, "Zhitu", and technical route for China FAW’s intelligent connected vehicles. In addition, he designed the HongQi L3 prototype vehicle and the forward-looking R&D of smart city intelligent automobiles.
Prof. Frank Zhao is the honorable lifetime president of FISTIA and a Professor and Director of Automotive Strategy Research Institute at Tsinghua University, China (since May 2013) where he leads a strategic research group on automotive industry policy, corporate management and technology strategies.
Prof. Zhao received a doctorate degree in Engineering from Hiroshima University in Japan in 1992 and has years of on-the-job experience in Japan, United Kingdom, United States, and China. Prior to joining Tsinghua University, Prof. Zhao had the experience of Vice President of Zhejiang Geely Holding Group, President of Zhejiang Geely Automotive R&D Center, President of Zhejiang Automotive Engineering Institute, and Chairman of DSI company of Australia since November 2006; Vice President of Shenyang Brilliance JinBei Automobile Company Limited and General Manager of its R&D Centre since 2004; and Engineering Specialist and Research Executive of Technical Affairs at DaimlerChrysler since 1997.
At Daimler Chrysler, Prof. Zhao was responsible for providing technical guidance and advice to product team managers and engineers within the corporation, relating to engine development issues and advance Powertrain technologies. He represented the Chrysler Group in various consortium activities. Prof. Zhao led the development of nearly 20 passenger cars, SUVs and more than 10 powertrains at Brilliance and Geely since his return to China in 2004. Also, Prof. Zhao had extensive experience in international acquisitions and overseas operations, including Geely's acquisition of British Manganese Bronze Holdings, Australian DSI Holdings, and Volvo Car Corporation. In addition, he was also heavily engaged in the strategic collaboration with these companies.
Prof. Zhao has published five books and more than 300 technical papers in English, Japanese and Chinese, and owned more than 200 patents. Prof. Zhao received many recognizations in his career including but not limited to the 2001 SAE Forest R. McFarland Award, Fellow of SAE in 2006, China Automobile Industry Outstanding Person in 2008, CTO of the Year in 2008 and Executive of the Year for Product Planning in 2009 by China Automotive News, the Silver Medal of National Scientific and Technological Progress in 2009, the Gold Medal of China Automotive Science and Technology Progress in 2012, and the Gold Medal of China Enterprise Management in 2012.
Xiangmo Zhao received the B.S. degree from Chongqing University, Chongqing, China, in 1987, and the M.S. and Ph.D. degrees from Chang'an University, Xi'an, China, in 2002 and 2005, respectively. He is currently a Professor with the School of Information Engineering, Chang'an University, and the President of Xi'an Technological University, Xi'an. He is a Vice President of the Joint Laboratory for Connected vehicles, Ministry of Education and China Mobile, and the Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Centre, and is also a Leader of the National Key Subjects-Traffic Information Engineering and Control, Chang'an University. His research interests include connected vehicles, automated vehicles, intelligent transportation systems, and computer science. He is the Director of the Information Professional Committee, a member of the Advisory Expert Group of the China Transportation Association, member of the National Motor Vehicle Operation Safety Testing Equipment Standardization Committee and the Leading Group of the National Traffic Computer Application Network, the Vice Chairperson of the Institute of Highway Association on Computer Professional Committee, and the Deputy Director of the Institute of Computer in Shaanxi Province.
Raj received his BEng and PhD degrees from City University of London in 1994 and 1999 respectively. From 1999 he worked at City University of London as a Research Fellow. In August 2000 he moved to CMG as a Telecommunications Consultant. After a few years in the industry Raj is now a Professor of Security Engineering. He is currently the Director of the Institute for Cyber Security at City University of London and carries out research in the areas of privacy preserving data management, Internet of Things privacy, network intrusion detection, cloud security and identity management using blockchain. Raj has received funding from EPSRC, Royal Academy of Engineering, European Commission, Innovate UK, British Council and industry to carry out research in cyber security. He has supervised several PhDs jointly with British Telecommunications, UK in the area if data analytics for cyber security and network intrusion detection. To date Raj has graduated more than 30 PhD students in the area of Cyber Security and continues to supervise a good number of students in the area of Cloud Security, Privacy and blockchain.
He is also the Programme Director for the MSc in Project Management, Finance and Risk.
He is a senior member of IEEE, a member of IET and an associate member of the charted institute of information security and a member of Technical Programme Committees of several security and privacy conferences.
Raj is the CEO of CityDefend (www.citydefend.com) a start up in the area of secure search in the encrypted Cloud. He also sits on the Editorial boards of the Journal on Wireless Networks, Elsevier Journal of Health Policy and Technology and Emerald Journal of Information Management and Computer Security.
Born 1969, he studied Mechanical Engineering at the University of Technology Graz, where he graduated 1995. He received a doctor’s degree in technical sciences in 1998 with distinction.
From 1998 to 2007 Arno Eichberger was employed at MAGNA STEYR Fahrzeugtechnik AG&Co and dealt with different aspects of active and passive safety.
Since 2007, Arno Eichberger is employed at the Institute of Automotive Engineering (University of Technology Graz) as vice-director of the Institute and head of the research area Vehicle Dynamics. His researches include development and testing of automated driving, human-machine interaction, vehicle dynamics control and suspension development.
Since 2012 he is Associate Professor holding a venia docendi in Automotive Engineering.
Siraj Shaikh is a Professor in Systems Security at Swansea University (UK). His research interests lie at the intersection of cybersecurity, systems engineering and computer science addressing cyber-physical systems security for automotive and transport systems. He is also Co-Founder and Chief Scientist at CyberOwl, which is dedicated to risk analytics and security monitoring for the maritime sector.
Siraj is also currently a Visiting Professor in the Research Group on Security, Risks Management and Conflict (SEGERICO) at Nebrija University (Spain).
Previously, he has also held Royal Academy of Engineering’s Industry Fellowship hosted at HORIBA MIRA (2015-2016). His research has been funded by EPSRC, ESRC, Lloyds Register Foundation (LRF), DSTL, British Council and Royal Academy of Engineering (RAEng).
Siraj has been involved in research, development and evaluation of large-scale distributed secure systems for over twenty-two years. His doctoral and post-doctoral research involved design and verification of security and safety-critical systems.
Siraj has recently co-authored a book on Formal Methods for Software Engineering Languages, Methods, Application Domains published by Springer (2022).
Dr. Xianyuan Zhan is a research associate professor at the Institute for AI Industry Research (AIR), Tsinghua University, with a joint appointment as a Young Researcher at the Shanghai Artificial Intelligence Laboratory. Before joining AIR, Dr. Zhan was a data scientist at JD Technology and also a researcher at Microsoft Research Asia (MSRA). His research directions include data-driven decision-making, embodied AI, and autonomous driving. He has published more than 70 papers in key conferences and journals in the field of Computer Science and Transportation Engineering, and 20 granted patents. He is also a committee member of Artificial Intelligence & Pattern Recognition Committee (CCF-AI) and Intelligent Vehicles Committee (CCF-IV) of China Computer Federation (CCF). He was also selected as one of the Top 50 Global AI Young Chinese Scholars (in the field of AI+X) by Baidu in 2022.
Dr. Zhu, Feng is an Associate Professor in the School of Civil and Environmental Engineering at Nanyang Technological University (NTU), Singapore. He received his Ph.D. degree from Purdue University in 2016, an M.Phil. degree from the Hong Kong University of Science and Technology in 2011, and a B.E. degree from Sun Yat-sen University in 2009. Dr. Zhu’s research focuses on urban mobility, connected and automated vehicles, traffic safety, and data analytics. His work has been published in leading SCI journals in transportation research. His research has received funding from a range of agencies and institutions, such as Singapore’s Ministry of Education, the Land Transport Authority, the Urban Redevelopment Authority, the National Environment Agency, and the Surbana Jurong–NTU Corporate Lab.
Jose Alvarez is Director of Research at NVIDIA, where he leads the Autonomous Driving Applied Research Team. His work focuses on scaling deep learning and advancing its application to safe and reliable autonomous vehicles.
Prior to joining NVIDIA, Dr. Alvarez held research positions at the Toyota Research Institute and NICTA (Australia). He earned his Ph.D. in Barcelona under the supervision of Prof. Antonio López and Prof. Theo Gevers, and later completed a postdoctoral fellowship at New York University with Prof. Yann LeCun.
Dr. Alvarez is a recipient of multiple awards in the field of autonomous driving and has published over 100 papers in top-tier machine learning and computer vision venues, accumulating more than 22,000 citations. He regularly serves as an Area Chair for leading conferences such as NeurIPS, ECCV, KDD, and ICRA, led the organization of several prominent workshop series, including DeepVision and the Workshop on Autonomous Driving; and sits on the editorial boards of IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Pattern Analysis and Machine Intelligence.
Moritz Werling is a Principal Engineer at BMW with over 20 years of experience in automated driving. During his doctoral studies at the Karlsruhe Institute of Technology (KIT), he focused on motion planning and control and spent part of his PhD as a visiting researcher at Stanford University. The results of his research have been implemented in numerous production applications. Since 2011, he has been working as a developer for automated driving at BMW AG. He has also been a lecturer on automated driving at KIT since 2015 and at the Technical University of Munich (TUM) since 2024. Since 2019, his work has focused on developing processes and methods for the design, verification, and validation of safe automated driving systems.
Siraj Shaikh is a Professor in Systems Security at Swansea University, where his research interests sit at the interface of automotive cybersecurity, systems engineering and cyber-physical systems security. He is the Chair of the Research and Innovation SIG at UK’s Automotive Electronics Systems Innovation Network (AESIN) and also the Safety and Security Workstream of the Global Automotive Advisory Council at SEMI (the leading semiconductor industry body). Siraj is a Certified Automotive Cyber-Security Professional, SGS-TÜV (2022), relating to ISO/SAE 21434 standard: extensive knowledge of its content; extensive experience of the interpretation of its requirements, its recommendations and its permissions; and extensive technical understanding of the methodology to implement its requirements. Siraj is well published in automotive cybersecurity, and he currently is a Member of the College of Experts part of the UK’s Department for Transport (DfT) Scientific Advisory Council (SAC). Previously, he has been an Industrial Fellow to HORIBA MIRA, funded by the Royal Academy of Engineering (RAEng). More recently he served as an Independent Scientific Adviser to The Alan Turing Institute under the BridgeAI initiative. He has also served as a RITICS Fellow on Economics of CNI Security at Imperial College (UK).
Dr. Dennis Kengo Oka is an automotive cybersecurity expert with nearly 20 years of global experience in the automotive industry. He received his Ph.D. in automotive security focusing on solutions for the connected car. As Global Technical & Cybersecurity Advisor at IAV, he focuses on cybersecurity solutions for software-defined vehicles, both on-road and off-road vehicles. Dennis has over 80 publications consisting of conference papers, journal articles and books, and is a frequent public speaker at international automotive and cybersecurity conferences and events. His latest published books are "Building Secure Cars: Assuring the Automotive Software Development Lifecycle" (Wiley, 2021), and "Building Secure Automotive IoT Applications: Developing Robust IoT Solutions for Next-Gen Automotive Software" (Packt, 2024).
Yufeng Li, Professor and PhD Supervisor at Shanghai University, serves as the head of the network space security program at Shanghai University. He is the academic leader in Intelligent Connected Vehicle (ICV) network security at Purple Mountain Laboratory and Songshan Laboratory, and the director of the Shanghai ICV Network Security Industrial Collaborative Innovation Center. Additionally, he holds the position of Executive Director of the Shanghai Key Laboratory of ICV Network Security. He is recognized as an expert in the National Key Special Project on New Energy Vehicles and serves as the deputy director of the ICV Safety Committee under the China Automotive Engineering Society, as well as the deputy director of the Artificial Intelligence Committee of the China Network Space Endogenous Security Alliance.
Yufeng Li has long dedicated his research to vehicle networking security and ICV security . His notable achievements include authoring 3 monographs, publishing over 100 papers, and securing 42 national invention patents. He has received 4 provincial or ministerial-level technology invention and scientific progress first prizes and 2 second prizes. His work focuses on advancing cybersecurity frameworks for smart vehicles and networked transportation systems.
Tariq Javaid is the Founder and Director of ECOSAR Tech, specializing in embedded system development for electric, connected, and autonomous vehicles. With over 20 years of automotive experience, including 15 years at Vector UK, he has expertise in AUTOSAR, model-based development, and scenario-based testing (MiL, SiL, HiL). His earlier career included roles at Aston Martin and JLR, where he contributed to complex ECU integration and electrical system validation. Tariq is passionate about advancing safety-critical systems and intelligent mobility through innovative validation methodologies. Tariq holds a BEng in Electronics and Communication Engineering from the University of Birmingham.
I graduated with a diploma in physics from Technical University of Dresden in 1986. For the next five years, I worked in nuclear safety research at Energiewerke Nord AG in Berlin. In 1991, I joined TÜV SÜD in Munich, where I worked in the field of risk, reliability and safety of human-machine systems in the industrial, railway and automotive sectors, recently as Chief Expert Automotive Safety. I’m engaged in the safety of automated driving since 2005.
I participated in the national standards committee for ISO 26262 and ISO 21448 and in national legislation on automated and autonomous driving. I am a guest lecturer at Ingolstadt University of Applied Sciences and at various private educational institutions.
In 2024, I co-founded and became managing director of TESACO GmbH. We advise on the development, approval and operation of vehicles and systems for Connected Cooperative Automated Mobility.
Jianru Xue earned his PhD from Xi'an Jiaotong University in 2003. From 2002 to 2003, he conducted collaborative research at Fuji Xerox Co., Ltd. in Japan. From 2008 to 2009, he was a visiting scholar at the University of California, Los Angeles. He is currently a professor and doctoral advisor at Xi'an Jiaotong University. His primary research areas include computer vision and pattern recognition, machine learning, and autonomous intelligent systems. He has co-authored one English-language monograph and published over 100 papers in domestic and international journals and conferences. He has received one National Natural Science Award Second Prize and one National Invention Award Second Prize. He has been selected as a National Leading Talent in Science and Technology Innovation and an Outstanding Young Talent of the Ministry of Education. He has also received the IEEE Intelligent Transportation Society Outstanding Research Team Award, the Chinese Automation Society Young Scientist Award, the Shaanxi Province Young Science and Technology Award, and the Shaanxi Province Young Pioneer Award. He currently serves as the Chair of the Hybrid Intelligence Professional Committee of the Chinese Automation Society, a Council Member of the China Society of Graphics and Imaging, and the Vice Chair of the Visual Big Data Professional Committee.
Hafiz Malik is Professor of Electrical and Computer Engineering (ECE) at the University of Michigan – Dearborn. His research in the areas of automotive cybersecurity, deepfakes, IoT security, sensor security, multimedia forensics, steganography/steganalysis, information hiding, pattern recognition, and information fusion is funded by the National Science Foundation, National Academies, Ford Motor Company, Marelli, Inc. N.A., and other agencies. He is a recipient of the UM – Dearborn 2022 Distinguished Research Award and College of Engineering and Computer Science 2020 Excellence in Research Award. He has published over 150 articles in leading peer-reviewed journals, conferences, and workshops. He is a founding member and chief operating officer (COO) of the Global Foundation for Cyber Studies and Research, a founding member of the Cybersecurity Center for Research, Education, and Outreach at UM-Dearborn and member of leadership circle for the Dearborn Artificial Intelligence Research Center at UM-Dearborn. He is also a member of the Scientific and Industrial Advisory Board (SIAB) of the National Center of Cyber Security Pakistan. He is a member of MCity Working Group on Cybersecurity, since 2015.
