The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a precomputed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.

With the increasing complexity of automated driving features, it is crucial to adopt innovative approaches that combine hardware and software to validate prototype vehicles in the early stages of development. This article demonstrates the effectiveness of a Vehicle-in-the-Loop (ViL) testbed in conducting dynamic tests of vehicles equipped with highly automated driving functions. The tests are designed to replicate critical driving scenarios from real-world environments on the ViL testbed. In this study, the Apollo platform is utilized to develop an automated driving function that can perceive the surrounding traffic in a virtual environment and generate feasible trajectories. This is achieved with the help of a multibody simulation platform. The control commands from the simulated driving function are then transmitted to the real vehicle to execute the planned action. The results demonstrate that critical traffic scenarios can be replicated more safely and repeatedly on the ViL testbed. Meanwhile, the Apollo-based driving function can effectively and comfortably cope with critical scenarios. Importantly, this study marks a significant milestone for the Apollo platform as it is implemented in a real-time system and tested on a ViL testbed.

The advent of active safety systems calls for the development of appropriate testing methods that are able to assess their capabilities to avoid accidents or lower impact speeds and thus, to mitigate the injury severity. Up to now the assessment is mostly based on the decrease of the collision speed due to CMS (collision mitigation systems). In order to assess the effects on injury severity developing methods, that are able to predict collision parameters correlating with the risk of getting injured, such as delta-v, for different impact situations is a mandatory task. In this study a momentum based impact model is assessed in terms of reliability to solve the collision mechanics and therefore to predict delta-v for frontal car collisions. Real accidents were re-simulated using pre-defined input parameters for the impact model (virtual forward simulation – VFS). Subsequently the impact model was analyzed for its sensitivity to specific input parameters. It was shown that VFS works for full impacts while improvements and optimizations are required for impacts that include a sliding movement in the contact zone of the vehicles.

This paper focuses on a novel concept for a fully electric city bus and the crashworthy integration of a changeable battery system. The changeable battery had to be located in an accessible and safe position. By careful analysis, an acceptable battery location was found under the floor of the vehicle. Due to the absence of design drawings of the real bus, an existing finite element model of a full size van was taken as a substitute. This model has less weight than the actual bus. Therefore, the battery was downsized based on range calculations. All parts of the vehicle concerning the combustion engine were removed and the battery was implemented in the model. This vehicle model was tested virtually using different crash load cases to demonstrate the feasibility of a crashworthy integration. Additional testing was carried out to evaluate the stability of the real bus lithium-ion cells under crash conditions.

The risk being severely or fatally injured in crashes with heavy goods vehicle and buses is much higher compared to other vehicles. Especially vulnerable road users such as pedestrian and cyclists are at high risk. In the European Union 4600 pedestrian and more than 2000 cyclists were killed in 2019. 18% of the fatalities were counted in crashes with heavy goods vehicles or buses. Blind spot situations and driver inattention or distraction are causing and contributing crash factors. Driver assistance systems are intended to support drivers and might have a positive effect on the crash avoidance. The objective of the study is the analysis of the change of driver behaviour in heavy goods vehicles and buses due to a aftermarket blind spot monitoring system. In a naturalistic driving study 15 heavy goods vehicles and five buses were equipped with a blind spot monitoring system and data were collected over a period of two years. The results revealed that the system would reduce the number of warnings with vulnerable road users by one third for heavy goods vehicles and 10% for buses. Up to 200 lives annually could potentially be saved with the analysed system, on the assumption that the collision warnings correlate directly with crashes.