In recent years, mobility on demand has experienced a major revival due to various ride-hailing companies entering the market. Competing in this field requires an efficient operation. Therefore, the applied policy, which cares for vehicle-to-customer assignment and vehicle repositioning, has to achieve good customer service and minimize cost while trying to keep the impact on the environment as low as possible. A promising approach is to coordinate the control of the entire fleet, which is foreseen to become even easier with the possibility of autonomous vehicles in mind. Anticipating future demand requires a good understanding of the spatiotemporal distributions of request origins and destinations, and the resulting imbalance between vehicle demand and availability. This results from a multitude of topological, demographic, and social effects, which are almost impossible to sufficiently capture in a handcrafted model of reasonable complexity. This can be circumvented by leveraging machine learning approaches. In this paper, an image-like representation of the city and its fleet's state is introduced. It is comprehensive and intuitive to use as input to convolutional neural networks, which in the past have already been proven to capture spatial relationships very well. This allows operating on realistic, full-sized traffic networks without greatly increasing the number of parameters the neural network has to learn and, hence, keeps the training effort low. Additionally, this state is combined with a similarly constructed repositioning action, reflecting a 2D distribution of a well-performing operational policy. This approach allows replacement of complex, handcrafted mathematical models by a single, compact, auto-encoder-like neural network.

In unit-capacity mobility-on-demand systems, the vehicles transport only one travel party at a time, whereas in ride-sharing mobility-on-demand systems, a vehicle may transport different travel parties at the same time, e.g., if paths are partially overlapping. One potential benefit of ride sharing is increased system efficiency. However, it is not clear what the trade-offs are between the efficiency gains and the reduction in quality of service. To quantify those trade-offs, an open-source simulation environment is introduced, which is capable of evaluating a large class of operational policies for ride-sharing mobility-on-demand systems. The impact of ride sharing on efficiency and service level is assessed for several benchmark operational policies from the literature and for different transportation scenarios: first a dense urban scenario, then a line-shaped, rural one. Based on the results of these case studies, we find that the efficiency gains in ride sharing are relatively small and potentially hard to justify against quality of service concerns such as reduced convenience, loss of privacy, and higher total travel and drive times. Furthermore, in the assessed scenarios, the relatively low occupancy of the vehicles suggests that smaller vehicles with 4-6 seats, able to handle occasional ride sharing, may be preferable to larger and more expensive vehicles such as minibuses.

This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers' travel time and vehicles' mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

This paper presents Skye, a novel blimp design. Skye is a helium-filled sphere of diameter 2.7m with a strong inelastic outer hull and an impermeable elastic inner hull. Four tetrahedrally-arranged actuation units (AU) are mounted on the hull for locomotion, with each AU having a thruster which can be rotated around a radial axis through the sphere center. This design provides redundant control in the six degrees of freedom of motion, and Skye is able to move omnidirectionally and to rotate around any axis. A multi-camera module is also mounted on the hull for capture of aerial imagery or live video stream according to an ‘eyeball’ concept — the camera module is not itself actuated, but the whole blimp is rotated in order to obtain a desired camera view. Skye is safe for use near people — the double hull minimizes the likelihood of rupture on an unwanted collision; the propellers are covered by grills to prevent accidental contact; and the blimp is near neutral buoyancy so that it makes only a light impact on contact and can be readily nudged away. The system is portable and deployable by a single operator — the electronics, AUs, and camera unit are mounted externally and are detachable from the hull during transport; operator control is via an intuitive touchpad interface. The motivating application is in entertainment robotics. Skye has a varied motion vocabulary such as swooping and bobbing, plus internal LEDs for visual effect. Computer vision enables interaction with an audience. Experimental results show dexterous maneuvers in indoor and outdoor environments, and non-dangerous impacts between the blimp and humans.