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1. Physics-guided Energy-efficient Path Selection Using On-board Diagnostics Data

   https://doi.org/10.1145/3406596  

   Li, Yan; Kotwal, Pratik; Wang, Pengyue; Xie, Yiqun; Shekhar, Shashi; Northrop, William (October 2020, ACM/IMS Transactions on Data Science)

   null (Ed.)

   Given a spatial graph, an origin and a destination, and on-board diagnostics (OBD) data, the energy-efficient path selection problem aims to find the path with the least expected energy consumption (EEC). Two main objectives of smart cities are sustainability and prosperity, both of which benefit from reducing the energy consumption of transportation. The challenges of the problem include the dependence of EEC on the physical parameters of vehicles, the autocorrelation of the EEC on segments of paths, the high computational cost of EEC estimation, and potential negative EEC. However, the current cost estimation models for the path selection problem do not consider vehicles’ physical parameters. Moreover, the current path selection algorithms follow the “path + edge” pattern when exploring candidate paths, resulting in redundant computation. Our preliminary work introduced a physics-guided energy consumption model and proposed a maximal-frequented-path-graph shortest-path algorithm using the model. In this work, we propose an informed algorithm using an admissible heuristic and propose an algorithm to handle negative EEC. We analyze the proposed algorithms theoretically and evaluate the proposed algorithms via experiments with real-world and synthetic data. We also conduct two case studies using real-world data and a road test to validate the proposed method. 

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2. Online predictive connected nd automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

   https://doi.org/https://doi.org/10.1016/j.trb.2020.12.009  

   Zhao, S; Zhang, K. (March 2021, Transportation research)

   null (Ed.)

   For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project. 

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3. Computationally Efficient Approach for Evaluating Eco-Approach and Departure for Heavy-Duty Trucks

   https://doi.org/10.1177/03611981241254112  

   Wei, Zhensong; Hao, Peng; Kailas, Aravind; Amar, Pascal; Palmeter, Kyle; Levin, Lennard; Orens, Stephen; Barth, Matthew; Boriboonsomsin, Kanok (December 2024, Transportation Research Record: Journal of the Transportation Research Board)

   Connected vehicle-based eco-driving applications have emerged as effective tools for improving energy efficiency and environmental sustainability in the transportation system. Previous research mainly focused on vehicle-level or link-level technology development and assessment using real-world field tests or traffic microsimulation models. There is still high uncertainty in understanding and predicting the impact of these connected eco-driving applications when they are implemented on a large scale. In this paper, a computationally efficient and practically feasible methodology is proposed to estimate the potential energy savings from one eco-driving application for heavy-duty trucks named Eco-Approach and Departure (EAD). The proposed methodology enables corridor-level or road network–level energy saving estimates using only road length, speed limit, and travel time at each intersection as inputs. This technique was validated using EAD performance data from traffic microsimulation models of four trucking corridors in Carson, California; the estimates of energy savings using the proposed methodology were around 1% average error. The validated models were subsequently applied to estimate potential energy savings from EAD along truck routes in Carson. The results show that the potential energy savings vary by corridor, ranging from 1% to 25% with an average of 14%. 

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4. VehiPose: a multi-scale framework for vehicle pose estimation

   https://doi.org/10.1117/12.2595800  

   Gupta, Divyansh; Artacho, Bruno; Savakis, Andreas (July 2021, Applications of Digital Image Processing XLIV)

   Tescher, Andrew G.; Ebrahimi, Touradj (Ed.)

   Vehicle pose estimation is useful for applications such as self-driving cars, traffic monitoring, and scene analysis. Recent developments in computer vision and deep learning have achieved significant progress in human pose estimation, but little of this work has been applied to vehicle pose. We propose VehiPose, an efficient architecture for vehicle pose estimation, based on a multi-scale deep learning approach that achieves high accuracy vehicle pose estimation while maintaining manageable network complexity and modularity. The VehiPose architecture combines an encoder-decoder architecture with a waterfall atrous convolution module for multi-scale feature representation. Our approach aims to reduce the loss due to successive pooling layers and preserve the multiscale contextual and spatial information in the encoder feature representations. The waterfall module generates multiscale features, as it leverages the efficiency of progressive filtering while maintaining wider fields-of-view through the concatenation of multiple features. This multi-scale approach results in a robust vehicle pose estimation architecture that incorporates contextual information across scales and performs the localization of vehicle keypoints in an end-to-end trainable network. 

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5. Trajectory-aware Lowest-cost Path Selection: A Summary of Results

   https://doi.org/10.1145/3340964.3340971  

   Li, Yan; Kotwal, Pratik; Wang, Pengyue; Shekhar, Shashi; Northrop, William (August 2019, SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases)

   The trajectory-aware lowest-cost path selection problem aims to find the lowest-cost path using trajectory data. Trajectory data is valuable since it carries information about travel cost along paths, and also reflects travelers' routing preference. Path-centric travel cost estimation models using trajectory data grows popular recently, which considers the auto-correlation of the energy consumption on different segments of a path. However, path-centric models are more computationally expensive than edge-centric models. The main challenge of this problem is that the travel cost of every candidate path explored during the process of searching for the lowest-cost path need to be estimated, resulting in high computational cost. The current path selection algorithms that use path-centric cost estimation models still follow the pattern of "path + edge" when exploring candidate paths, which may result in redundant computation. We introduce a trajectory-aware graph model in which each node is a maximal trajectory-aware path. Two nodes in the trajectory-aware graph are linked by an edge if their union forms a trajectory-union path. We then propose a path selection algorithm to find a path in the proposed trajectory-aware graph which corresponds to the lowest-cost path in the input spatial network. We prove theoretically the proposed algorithm is correct and complete. Moreover, we prove theoretically that the proposed path selection algorithm cost much less computational time than the algorithm used in the related work, and validate it through experiments using real-world trajectory data. 

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