Related papers: Simulation-based Validation for Autonomous Driving…
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world…
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising…
Driving simulation plays a crucial role in developing reliable driving agents by providing controlled, evaluative environments. To enable meaningful assessments, a high-quality driving simulator must satisfy several key requirements:…
Autonomous driving promises safer roads, reduced congestion, and improved mobility, yet validating these systems across diverse conditions remains a major challenge. Real-world testing is expensive, time-consuming, and sometimes unsafe,…
Self-driving technology is expected to revolutionize different sectors and is seen as the natural evolution of road vehicles. In the last years, real-world validation of designed and virtually tested solutions is growing in importance since…
The development of Autonomous Vehicles (AVs) has made significant progress in the last years. An important aspect in the development of AVs is the assessment of their safety. New approaches need to be worked out. Among these, real-world…
Formal verification provides strong safety guarantees but only for models of cyber-physical systems. Hybrid system models describe the required interplay of computation and physical dynamics, which is crucial to guarantee what computations…
In the past two decades, autonomous driving has been catalyzed into reality by the growing capabilities of machine learning. This paradigm shift possesses significant potential to transform the future of mobility and reshape our society as…
Control systems on unmanned vehicles are safety-critical systems whose requirements on reliability and safety are ever-increasing. Currently, testing a complex autonomous control system is an expensive and time-consuming process, which…
In the recent years, there has been a rush towards highly autonomous systems operating in public environments, such as automated driving of road vehicles, passenger shuttle systems and mobile robots. These systems, operating in…
Increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation…
Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated driving is scenario-based testing. However, it introduces the problem of what…
Automated Vehicles (AVs) are rapidly maturing in the transportation domain. However, the complexity of the AV design problem is such that no single technique is sufficient to provide adequate validation of key properties such as safety,…
Testing of autonomous systems is extremely important as many of them are both safety-critical and security-critical. The architecture and mechanism of such systems are fundamentally different from traditional control software, which appears…
Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough for its acceptance on the…
Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane…
Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing…
Ensuring the quality of automated driving systems is a major challenge the automotive industry is facing. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, automated vehicles at…
Scenario-based testing is envisioned as a key approach for the safety assurance of autonomous vehicles. In scenario-based testing, relevant (driving) scenarios are the basis of tests. Many recent works focus on specification, variation,…
For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With…