This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. 

/pdf/analysis-and-control-of-autonomous-mobility-on-demand-15hb4fy8.pdf

Analysis and Control of Autonomous Mobility-on-Demand Systems

Understanding and predicting human motion behavior with social interactions have become an increasingly crucial problem for a vast number of applications, ranging from visual navigation of autonomous vehicles to activity prediction of intelligent video surveillance. Accurately forecasting crowd motion behavior is challenging due to the multimodal nature of trajectories and complex social interactions between humans. Recent algorithms model and predict the trajectory with a single resolution, making them difficult to exploit the long-range information and the short-range information of the motion behavior simultaneously. In this paper, we propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. The hierarchical design of our framework allows to model the trajectory with multi-resolution, then can better capture the motion behavior at various tempos. By progressively combining the global context with the local one, we finally construct a coarse-to-fine hierarchical pedestrian trajectory prediction framework with multi-supervision. Further, we introduce a unified spatial-temporal attention mechanism to adaptively select important information of persons around in both spatial and temporal domains. We show that our attention strategy is intuitive and effective to encode the influence of social interactions. Experimental results on two benchmarks demonstrate the superiority of our proposed scheme. 

Temporal Pyramid Network With Spatial-Temporal Attention for Pedestrian Trajectory Prediction

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach

https://www.mdpi.com/2079-9292/9/6/1021/pdf

A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich

Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the emerging concept of Autonomous Mobility-on-Demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems. Specifically, we first identify problem settings for their analysis and control, both from the operational and the planning perspective. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.

Analysis and Control of Autonomous Mobility-on-Demand Systems: A Review.

Robotic multi-agent systems can efficiently handle spatially distributed tasks in dynamic environments. Problem instances of particular interest, and generality are the dynamic traveling repairman problem, and the dynamic vehicle routing problem. Operational policies for robotic fleets solving these two problems take decisions in an online setting with continuously arriving demands to optimize service level, and efficiency, and can be classified along several lines. First, some require a model of the demand, e.g., based on historical information, while others work model-free. Second, they are designed for different operating conditions from light to heavy system load. Third, they work in a time-invariant or time-varying setting. We present a novel class of model-free operational policies for time-varying demands, which have performance independent of the load factor, a combination of properties not achieved by other operational policies in the literature. The underlying principle of the introduced policies is to send available robots to recent service request locations. In simple terms, they rely on sending more than one robot for every service request arriving to the system. This leads to an advantage in scenarios where demand is non-uniformly distributed, and correlated in space, and time. We provide performance guarantees for both the time-invariant, and the time-varying cases as well as for correlated demand. We verify our theoretical results numerically. Finally, we apply our operational policy to the problem of mobility-on-demand fleet operation, and demonstrate that it outperforms model-based, and complex algorithms across all load ranges, despite its simplicity.

/pdf/the-1-method-model-free-adaptive-repositioning-policies-for-3oo7lfo5s3.pdf

The +1 Method: Model-Free Adaptive Repositioning Policies for Robotic Multi-Agent Systems

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.

Joel Gachter

Papers

The +1 Method: Model-Free Adaptive Repositioning Policies for Robotic Multi-Agent Systems

Image Representation of a City and Its Taxi Fleet for End-To-End Learning of Rebalancing Policies