A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced

Order-picking workstations for automated warehouses

The FALCON (Flexible Automated Logistic CONcept) project aims at the development of a new generation of warehouses and distribution centers with a maximum degree of automation. As part of the FALCON project, this dissertation addresses the design and analysis of (automated) workstations in warehouses with an end-of-aisle order-picking system (OPS). Methods are proposed for architecting, quantifying performance, and controlling such a system. Four main topics are discussed in this dissertation. First, a modular architecture for an end-of-aisle OPS with remotely located workstations is presented. This architecture is structured into areas and operational layers. A hierarchical decentralized control structure is applied. A case of an industrial-scale distribution center is presented to demonstrate the applicability of the proposed architecture for performance analysis using the process algebra-based simulation language $\chi$ (Chi). Additionally, it is demonstrated how the architecture allows straightforward modification of the systems configurations, design parameters, and control heuristics. Second, a method to quantify the operational performance of order-picking workstations has been developed. The method is based on an aggregate modeling representation of the workstation using the EPT (Effective Process Time) concept. A workstation is considered in which a human picker is present to process one customer order at a time while products for multiple orders arrive simultaneously at the workstation. The EPT parameters are calculated from arrival and departure times of products using a sample path equation. Two model variants have been developed, namely for workstations with FCFS (First-Come-First-Serve) and for workstations with non-FCFS processing of products and orders. Both models have been validated using data from a real, operating workstation. The results show that the proposed aggregate modeling methodology gives good accuracy in predicting product and order flow time distributions. Third, the dissertation studies the design and control of an automated, remotely located order-picking workstation that is capable of processing multiple orders simultaneously. Products for multiple orders typically arrive out-of-sequence at the workstation as they are retrieved from dispersed locations in the storage area. The design problem concerns the structuring of product/order buffer lanes and the development of a mechanism that overcomes out-of-sequence arrivals of products. The control problem concerns the picking sequence at the workstation, as throughput deteriorates when a poor picking sequence is applied. An efficient control policy has been developed. Its performance is compared to a number of other picking policies including nearest-to-the-head, nearest neighbor, and dynamic programming. Subsequently, the resulting throughput and queue length distribution are evaluated under different settings. Insights for design considerations of such a system are summarized. Finally, the dissertation reflects on the findings from the proposed methods and uses them to come up with comprehensive design principles of end-of-aisle OPS with remotely located workstations. The various issues influencing the performance of such a system are highlighted. Moreover, the contribution of each proposed method with regards to these issues is delineated

Optimal Energy Saving Adaptive Cruise Control in Overtaking Scenarios for a Hybrid Electric Vehicle

The overtaking planning problem plays a crucial role to foster the adaptive cruise control (ACC) technology. It reveals extremely challenging due to critical requirements on the real-time capability of the control system and on conflicting objectives for the longitudinal speed trajectory generated over time for the Following Vehicle (e.g. in terms of maneuver efficiency, passenger comfort, energy economy). In this paper, an approach to solve this problem is proposed by developing an optimal energy saving oriented ACC algorithm for overtaking scenarios considering a hybrid electric vehicle (HEV) as the Following Vehicle. An off-line optimization based on Dynamic Programming (DP) is implemented. The proposed DP formulation aims at controlling the Following Vehicle longitudinal jerk over time to minimize the overall HEV energy consumption throughout the overtaking maneuver. Optimization constraints are considered for the inter-vehicular distance between Leader Vehicle and Following vehicle over time, and for the operational limits of the HEV powertrain components. The developed ACC algorithm is demonstrated achieving up to 4.1% energy saving and significant improvements in terms of passenger comfort in different overtaking scenarios