Recent advancements in vehicle technology have stimulated innovation across the
automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving …

Traditional road testing of autonomous vehicles faces significant limitations, including long
testing cycles, high costs, and substantial risks. Consequently, autonomous driving …

The planning and control subsystem is essential to the autonomous driving system. The
prevailing algorithmic architecture follows a hierarchical design: the planning subsystem …

Uncertainty has long been a critical area of study in robotics, particularly when robots are
equipped with analytical models. As we move towards the widespread use of deep neural …

Thoroughly testing autonomy systems is crucial in the pursuit of safe autonomous driving
vehicles. It necessitates creating safety-critical scenarios that go beyond what can be safely …

Scenario simulation plays an integral role in the development, application, and management
of intelligent vehicles. However, planning agents and customizing scenarios for complex …

The development of autonomous driving models through reinforcement learning has gained
significant traction. However, developing obstacle avoidance systems remains a challenge …

Simulators generate diverse and realistic traffic scenarios to boost the development of
autonomous driving systems. However, existing simulators often fall short in scenario …

The phenomenon of urbanization, characterized by the migration of people from rural to
urban areas, has led to an expansion of existing urban challenges while introducing new …

Advanced Driver Assistance Systems (ADAS) and autonomous driving require high
positioning performance (high accuracy, reliability, and availability). These requirements are …