A5043254931- Autonomous Vehicle Technology and Safety
- FinTech, Crowdfunding, Digital Finance
- Safety Systems Engineering in Autonomy
- Edcuational Technology Systems
- Traffic control and management
- Risk and Safety Analysis
- Advanced Optical Sensing Technologies
- Real-time simulation and control systems
- Vehicular Ad Hoc Networks (VANETs)
- Software Reliability and Analysis Research
- Traffic and Road Safety
- Fire Detection and Safety Systems
- Vehicle Dynamics and Control Systems
- Human-Automation Interaction and Safety
Graz University of Technology
2022-2024
Virtual Vehicle (Austria)
2023
Scenario based methods for testing and validation of automated driving systems in virtual test environments are gaining importance becoming an important component verification processes systems. The high system complexity such the costs lead to exponential increase efforts real world testing. Recent research works have shown that it is necessary drive billions kilometers ensure safety reliability This amount far from possible achievable any procedure regarding time costs. Using different...
Vehicle safety promises to be one of the Advanced Driver Assistance System’s (ADAS) biggest benefits. Higher levels automation remove human driver from chain events that can lead a crash. Sensors play an influential role in vehicle driving as well ADAS by helping watch vehicle’s surroundings for safe driving. Thus, load is drastically reduced steering accelerating and braking long-term The baseline development future intelligent vehicles relies even more on fusion data surrounding sensors...
The safety approval and assessment of automated driving systems (ADS) are becoming sophisticated challenging tasks. Because the number traffic scenarios is vast, it essential to assess their criticality extract ones that present a risk. In this paper, we proposing novel method based on time-to-react (TTR) measurement, which has advantages in considering avoidance possibilities. incorporates concept fictive vehicles variable thresholds (VCTs) overall scenario's criticality. By introducing...
The advent of Large Language Models (LLM) provides new insights to validate Automated Driving Systems (ADS). In the herein-introduced work, a novel approach extracting scenarios from naturalistic driving datasets is presented. A framework called Chat2Scenario proposed leveraging advanced Natural Processing (NLP) capabilities LLM understand and identify different scenarios. By inputting descriptive texts conditions specifying criticality metric thresholds, efficiently searches for desired...
This conference paper presents the mid-term results of two EUREKA projects "testEPS" and "Central System". TestEPS addresses certification autonomous driving systems, while Central System focuses on infrastructure solutions for a connected vehicle system. Both integrate same four technological fields: simulation virtual testing, realworld HD mapping, communication, to realize their vision achieve individual goals. The application use cases each field as utilized in respective project....
Vehicles equipped with automated driving functions are increasingly present on roads. With more significant growth comes a greater need for compliance recognisable methods in testing proposed by standards and norms. As is accompanied demanding economic timing challenges, virtual promising approach, especially early development. This study uses scenario-based to analyse hazards regarding intended functionality (ISO PAS 21448) functional safety 26262). The primary benefit of this approach...