Automotive Innovation最新文献

Stricter CO₂ and local air quality targets are increasing the demand for electrified powertrains including hybrid electric vehicles (HEVs). The impact of an electrified vehicle powertrain on the catalytic performance of the emissions control system presents the challenge of multiple cold/warm starts during vehicle operation. This work investigates advanced energy efficient technologies to understand and enhance the catalytic reduction of primary and secondary emissions under challenging HEV operation conditions. A novel strategy based on the concept of engine cylinder deactivation is experimentally studied at various starting catalyst temperatures aiming to reduce the time required to reach catalyst light-off temperature and thus tailpipe emissions. Unregulated secondary emissions (NH₃ and N₂O) are also investigated, which are expected to become more pertinent in the future. This work demonstrates that operating under the studied strategy for a short period of time increased TWC temperature by up to 300 °C and reduced mass-based emissions of CO, NO, HCs, N₂O and NH₃. These findings are significant to inform the optimization of energy efficient catalyst heating strategies for HEVs in order to reduce both primary and secondary emissions.

Efficient exploration and understanding of an autonomous driving system's capabilities and functional boundaries are crucial for ensuring safety performance. This paper offers a comprehensive examination of safety verification and test case generation for autonomous driving function stacks, enhancing their safety and reliability. Firstly, we introduce a holistic approach that synergizes operational flow-oriented Hazard and Operability Study (HAZOP) with cascaded System-Theoretic Process Analysis (STPA) processes. Secondly, we propose a test case generation procedure that begins with an expansion to discrete parameters using tree search, followed by heterogeneous sampling in the continuous parameter space. Additionally, this paper features a real-world case study with WATonoBus, showcasing the practicality and effectiveness of the proposed methods in securing autonomous vehicles safe operation in complex urban settings. Our findings make a substantial contribution to the autonomous vehicle safety field, offering critical insights for ongoing research and development in this rapidly advancing area.

Scenario-based testing plays a pivotal role in the development and validation of automated vehicles. Its main challenge is to efficiently generate realistic and relevant test scenarios to identify and analyze shortcomings of automated driving systems. The Scenario Factory 2.0 unifies several scenario generation techniques from the open-source CommonRoad framework and introduces simulation modes for coupling with the traffic simulators OpenTrafficSim and SUMO. The simulation modes enable generating scenarios with a tunable similarity to existing ones. As existing approaches, the Scenario Factory 2.0 integrates scenario generation from formal specifications and falsification techniques. Scenario Factory 2.0 has a modular structure and the modules can be easily rearranged for creating required scenarios. We evaluate the effectiveness of the novel simulation modes for various traffic scenarios and demonstrate the scenario generation with Scenario Factory 2.0 in a use case. The open-source code is provided at https://commonroad.in.tum.de/tools/scenario-factory.