Abstract Automated Driving Systems (ADS) open up a new domain for the automotive
industry and offer new possibilities for future transportation with higher efficiency and …

When will automated vehicles come onto the market? This question has puzzled the
automotive industry and society for years. The technology and its implementation have …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

Automated vehicles (AVs) are widely considered to play a crucial role in future transportation
systems because of their speculated capabilities in improving road safety, saving energy …

Verification and validation (V&V) hold a significant position in the research and development
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Scenario-based approaches have been receiving a huge amount of attention in research
and engineering of automated driving systems. Due to the complexity and uncertainty of the …

One obstacle to applying reinforcement learning algorithms to real-world problems is the
lack of suitable reward functions. Designing such reward functions is difficult in part because …

The long short-term memory (LSTM) model is one of the most commonly used vehicle
trajectory predicting models. In this paper, we study two problems of the existing LSTM …