IEEE International Symposium on Autonomous Driving Software


Today’s automobile industry has faced three challenges: from the internal combustion engine to the electric engine, from human driver-based mechanical operations to software-enabled autonomous driving, and from the ownership business model to the mobility as a service. The transformation will not only reshape the automobile industry in the coming decade with the trillion-dollar new market opportunities but will also offer humanity a better future with cleaner energy consumption, cheaper and safer transportation services, and more efficient use of the urban infrastructures.

However, the application of immature and unreliable software in autonomous driving has caused fatal accidents resulting in property loss or even loss of life. Improving the dependability (including safety, security, reliability, etc.) of such software is very challenging. On one hand, software for autonomous vehicles must deal with highly dynamic and volatile environments. On the other hand, it needs to incorporate increasingly complicated new technologies based on the use of machine vision, motion sensors, image and voice recognition, and artificial intelligence to interact with surrounding environments and respond to the continuous changes for appropriate solutions.

The Technical Committee on Electric Autonomous Vehicles (EVA) of the IEEE Reliability Society will collaborate with the Tenth International Conference on Dependable Systems and Their Applications (DSA 2023) to organize a two-day workshop on August 10th to discuss techniques and experiences that can help practitioners develop highly dependable software for safe, secure, and reliable autonomous vehicles.


The list of topics includes, but is not limited to:

  • Autonomous vehicle software vulnerability assessment, risk analysis, attack, and threat models
  • Safe design and implementation of autonomous vehicle software, and applications for autonomous vehicles
  • Autonomous vehicle operating system security
  • Availability, resiliency and fault tolerance in autonomous vehicle software
  • Autonomous vehicle software testing, verification, and validation
  • Autonomous vehicle software quality evaluation methodologies and metrics
  • Autonomous vehicle software forensics, events monitoring, and auditing
  • Autonomous vehicle software standardization, certification, and interoperability
  • Secure intra-vehicle, vehicle-to-vehicle, and other vehicular network communications
  • Testing of machine learning components for autonomous vehicles
  • Practical experiences, empirical studies, and testbeds for autonomous vehicle software
  • Industrial experiences and best practices for autonomous vehicle software development
  • Program synthesis for autonomous vehicle software


Zijiang James Yang's avatar
Zijiang James Yang China

Xian Jiaotong University & GuardStrike Inc., China

Lei Bu's avatar
Lei Bu China

Nanjing University, China

Invited Speakers (To Be Announced)

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