Abstract-Lateral driver support systems have the potential to reduce the number of accidents associated with -both intentional and unintentional -lane departures. Additionally, such systems may increase driving comfort and stimulate a more efficient traffic flow, thereby reducing traffic emissions and the costs associated with traffic delays. This paper provides a literature review to identify the current state of the art on lateral driver support systems. The emphasis is on sensor technology, detection algorithms and safety assessment algorithms.

Transportation engineers are commonly faced with the question of how to extract information from expensive and scarce field data. Modeling the distribution of trips between zones is complex and dependent on the quality and availability of field data. This research explores the performance of neural networks in trip distribution modeling and compares the results with commonly used doubly constrained gravity models. The approach differs from other research in several respects; the study is based on both synthetic data, varying in complexity, as well as real-world data. Furthermore, neural networks and gravity models are calibrated using different percentages of hold out data. Extensive statistical analyses are conducted to obtain necessary sample sizes for significant results. The results show that neural networks outperform gravity models when data are scarce in both synthesized as well as real-world cases. Sample size for statistically significant results is forty times lower for neural networks.

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Vehicle automation opens opportunities toward the improvement of people, planet, profit value in applications on distribution centres (DCs). Despite vision and drive for innovation in logistics, the lack of knowledge prevents the application of autonomous applications. Intelligent Truck Applications in Logistics (INTRALOG) contributes to this deficit and generates valuable insights for a future application in public environments. Current operational automated guided truck applications are bound to fixed infrastructure and do neither operate in the public domain nor offer opportunities to do so. Nowadays, in-vehicle intelligent systems are focused on driver support, opening opportunities such as truck platooning. INTRALOG cross-borders on DCs in relatively low-complex traffic environment, bridging the gap between autonomous driving in the public domain. The multiagent system developed within INTRALOG aligns logistical movements on DCs, controlling single or double articulated container trailers (longer and heavier vehicle) between the public parking area and cross-docks. This study elaborates on the experiments on automated manoeuvring on a DC with single (SAVs) and double articulated vehicles (DAVs). The experiments comply with business requirements, e.g. manoeuvrability, time to dock and positioning accuracy. The research focuses on the effects of these aspects and control strategies of SAVs and DAVs.

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