Vehicle Ad Hoc Network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANET are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANET have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, Vehicle-to-Everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. We have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000s,14 key packets have not arrived in IEEE 802.11a and 8 packets have not arrived in IEEE 802.11p. at 8.0000s 38 key packets have not arrived in IEEE802.11a and 6 packets have not arrived in IEEE802.11p. Compare the performance of IEEE 802.11a and IEEE 802.11p.The study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.
车辆自组网（Vehicle Ad Hoc Network，VANET）已逐渐成为无线网络和智能车辆领域的一个突出研究课题。VANET 是一种独特的以车辆为移动节点的移动 ad hoc 网络，与传统的无线自组织网络相比，具有独特的性能特征。近年来，VANET 在无线网络和智能交通领域备受关注。作为自动驾驶技术的一个组成部分，车对物（V2X）通信跨越多个学科，与智能交通、辅助驾驶、主动安全和智能汽车密切相关。在现实世界中评估 VANET 协议和应用具有挑战性。因此，利用仿真工具进行 VANET 研究是一种有效的方法。我们设计并开发了一个优化平台，使用 IEEE 802.11a 和 IEEE 802.11p 协议在 NS-2 创建的模拟城市交通环境中进行通信。模拟结果证实了将 IEEE 802.11p 协议应用于无线车载 ad hoc 网络的可行性和合理性。在 300 米距离内，0.0000 秒时，14 个关键数据包未到达 IEEE 802.11a，8 个数据包未到达 IEEE 802.11p；8.0000 秒时，38 个关键数据包未到达 IEEE 802.11a，6 个数据包未到达 IEEE 802.11p。比较 IEEE 802.11a 和 IEEE 802.11p 的性能。研究得出结论，在城市移动环境中使用 802.11p 协议可以提高可靠性并减少数据包平均延迟。
{"title":"Simulation Test Research on Typical Simulator Ns-2 in Urban Vehicle Ad Hoc Network Based on Big Data","authors":"Xiaoting Wang, Junxia Jin, Zhan Zhao","doi":"10.1115/1.4064747","DOIUrl":"https://doi.org/10.1115/1.4064747","url":null,"abstract":"\u0000 Vehicle Ad Hoc Network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANET are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANET have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, Vehicle-to-Everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. We have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000s,14 key packets have not arrived in IEEE 802.11a and 8 packets have not arrived in IEEE 802.11p. at 8.0000s 38 key packets have not arrived in IEEE802.11a and 6 packets have not arrived in IEEE802.11p. Compare the performance of IEEE 802.11a and IEEE 802.11p.The study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"63 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vehicle Ad Hoc Network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANET are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANET have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, Vehicle-to-Everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. We have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000s,14 key packets have not arrived in IEEE 802.11a and 8 packets have not arrived in IEEE 802.11p. at 8.0000s 38 key packets have not arrived in IEEE802.11a and 6 packets have not arrived in IEEE802.11p. Compare the performance of IEEE 802.11a and IEEE 802.11p.The study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.
车辆自组网（Vehicle Ad Hoc Network，VANET）已逐渐成为无线网络和智能车辆领域的一个突出研究课题。VANET 是一种独特的以车辆为移动节点的移动 ad hoc 网络，与传统的无线自组织网络相比，具有独特的性能特征。近年来，VANET 在无线网络和智能交通领域备受关注。作为自动驾驶技术的一个组成部分，车对物（V2X）通信跨越多个学科，与智能交通、辅助驾驶、主动安全和智能汽车密切相关。在现实世界中评估 VANET 协议和应用具有挑战性。因此，利用仿真工具进行 VANET 研究是一种有效的方法。我们设计并开发了一个优化平台，使用 IEEE 802.11a 和 IEEE 802.11p 协议在 NS-2 创建的模拟城市交通环境中进行通信。模拟结果证实了将 IEEE 802.11p 协议应用于无线车载 ad hoc 网络的可行性和合理性。在 300 米距离内，0.0000 秒时，14 个关键数据包未到达 IEEE 802.11a，8 个数据包未到达 IEEE 802.11p；8.0000 秒时，38 个关键数据包未到达 IEEE 802.11a，6 个数据包未到达 IEEE 802.11p。比较 IEEE 802.11a 和 IEEE 802.11p 的性能。研究得出结论，在城市移动环境中使用 802.11p 协议可以提高可靠性并减少数据包平均延迟。
{"title":"Simulation Test Research on Typical Simulator Ns-2 in Urban Vehicle Ad Hoc Network Based on Big Data","authors":"Xiaoting Wang, Junxia Jin, Zhan Zhao","doi":"10.1115/1.4064747","DOIUrl":"https://doi.org/10.1115/1.4064747","url":null,"abstract":"\u0000 Vehicle Ad Hoc Network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANET are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANET have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, Vehicle-to-Everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. We have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000s,14 key packets have not arrived in IEEE 802.11a and 8 packets have not arrived in IEEE 802.11p. at 8.0000s 38 key packets have not arrived in IEEE802.11a and 6 packets have not arrived in IEEE802.11p. Compare the performance of IEEE 802.11a and IEEE 802.11p.The study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"91 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes a mutual-information-based approach that exploits a dynamics model to quantify and detect anomalies for applications such as autonomous vehicles. First, mutual information (MI) is utilized to quantify the level of uncertainty associated with the behaviors of the vehicle. The MI approach handles novel anomalies without the need for data-intensive training; and the metric readily applies to multivariate datasets for improved robustness, compared to for example, measures such as vehicle tracking error. Second, to further improve the response time of anomaly detection, the vehicle-dynamics model is used to create a predicted component that is combined with current and past measurements. This approach compensates for the lag in the anomaly detection process compared to strictly using current and past measurements. Finally, three different MI-based strategies are described and compared experimentally: anomaly detection using MI with (1) current and past measurements (reaction), (2) current and future information (prediction), and (3) a combination of past and future information (reaction-prediction) with three different time windows. The experiments demonstrate quantification and detection of anomalies in three driving situations: (1) veering off the road, (2) driving on the wrong side of the road, and (3) swerving within a lane. Results show that by anticipating the movements of the vehicle, the quality and response time of the anomaly detection is more favorable for decision-making while not raising false alarms compared to just using current and past measurements.
{"title":"Feedforward Mutual-Information Anomaly Detection: Application to Autonomous Vehicles","authors":"Sasha M. McKee, Osama Haddadin, Kam K. Leang","doi":"10.1115/1.4064519","DOIUrl":"https://doi.org/10.1115/1.4064519","url":null,"abstract":"\u0000 This paper describes a mutual-information-based approach that exploits a dynamics model to quantify and detect anomalies for applications such as autonomous vehicles. First, mutual information (MI) is utilized to quantify the level of uncertainty associated with the behaviors of the vehicle. The MI approach handles novel anomalies without the need for data-intensive training; and the metric readily applies to multivariate datasets for improved robustness, compared to for example, measures such as vehicle tracking error. Second, to further improve the response time of anomaly detection, the vehicle-dynamics model is used to create a predicted component that is combined with current and past measurements. This approach compensates for the lag in the anomaly detection process compared to strictly using current and past measurements. Finally, three different MI-based strategies are described and compared experimentally: anomaly detection using MI with (1) current and past measurements (reaction), (2) current and future information (prediction), and (3) a combination of past and future information (reaction-prediction) with three different time windows. The experiments demonstrate quantification and detection of anomalies in three driving situations: (1) veering off the road, (2) driving on the wrong side of the road, and (3) swerving within a lane. Results show that by anticipating the movements of the vehicle, the quality and response time of the anomaly detection is more favorable for decision-making while not raising false alarms compared to just using current and past measurements.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"87 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139606332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Humanoid robots must be capable of walking on complicated terrains and tackling a variety of obstacles leading to their wide range of possible implementations. To that aim, in this article, the issue of humanoid robots walking on uneven terrain and tackling static and dynamic obstacles is examined. It is inspected by implementing a novel Enhanced DAYANI Arc Contour Intelligent (EDACI) Algorithm that designs trajectory by searching feasible points in the environment. It provides an optimum steering angle, and step optimization is performed by BFGS (Broyden–Fletcher–Goldfarb–Shanno) Quasi-Newton method that leads to guide the humanoid robot stably to the target. The leg length policy has been presented and a reward-based system has been implemented in the walking pattern generator that provides the optimum gait parameters. One humanoid robot act as a dynamic obstacle to others if they are navigating on a single terrain. It may generate a situation of deadlock, which needs to be solved. In this article, a dining philosopher controller (DPC) is employed to deal with and solve this issue. Simulations are used to evaluate the proposed approach in several uneven terrains having two humanoid NAOs. The findings indicate that it can precisely and efficiently produce optimal collision-free paths, demonstrating its efficacy. Experiments in similar terrain are performed that validate the results with a deviation under 6 %. The energy efficiency of the developed controller is evaluated in reference to the inbuilt controller of NAO based on energy consumption. In order to check the feasibility and accuracy of the developed controller, a comparison with an established technique is provided.
{"title":"Stable Locomotion of Humanoid Robots on Uneven Terrain employing Enhanced DAYANI Arc Contour Intelligent Algorithm","authors":"A. Kashyap, D. Parhi","doi":"10.1115/1.4063055","DOIUrl":"https://doi.org/10.1115/1.4063055","url":null,"abstract":"\u0000 Humanoid robots must be capable of walking on complicated terrains and tackling a variety of obstacles leading to their wide range of possible implementations. To that aim, in this article, the issue of humanoid robots walking on uneven terrain and tackling static and dynamic obstacles is examined. It is inspected by implementing a novel Enhanced DAYANI Arc Contour Intelligent (EDACI) Algorithm that designs trajectory by searching feasible points in the environment. It provides an optimum steering angle, and step optimization is performed by BFGS (Broyden–Fletcher–Goldfarb–Shanno) Quasi-Newton method that leads to guide the humanoid robot stably to the target. The leg length policy has been presented and a reward-based system has been implemented in the walking pattern generator that provides the optimum gait parameters. One humanoid robot act as a dynamic obstacle to others if they are navigating on a single terrain. It may generate a situation of deadlock, which needs to be solved. In this article, a dining philosopher controller (DPC) is employed to deal with and solve this issue. Simulations are used to evaluate the proposed approach in several uneven terrains having two humanoid NAOs. The findings indicate that it can precisely and efficiently produce optimal collision-free paths, demonstrating its efficacy. Experiments in similar terrain are performed that validate the results with a deviation under 6 %. The energy efficiency of the developed controller is evaluated in reference to the inbuilt controller of NAO based on energy consumption. In order to check the feasibility and accuracy of the developed controller, a comparison with an established technique is provided.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117321929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Path planning has been a hot research topic in robotics and is a vital functionality for autonomous systems. As the time complexity of traditional path planning algorithms grows rapidly with the complexity of the problem, evolutionary algorithms are widely applied for their near-optimal solutions. However, evolutionary algorithms can be trapped in a local optimum or converge to infeasible solutions, especially for large search spaces. As the problem scale increases, evolutionary algorithms often cannot find feasible solutions with random exploration, making it extremely challenging to solve long-range path-planning problems in environments with obstacles of various shapes and sizes. For long-range path planning of an autonomous ship, the current downsampling map approach may result in the disappearance of rivers and make the problem unsolvable. This paper introduces a novel area-based collision assessment method for Genetic Algorithm (GA) that can always converge to feasible solutions with various waypoints in large-scale and obstacle-filled environments. Waypoint-based crossover and mutation operators are developed to allow GA to modify the length of the solution during planning. To avoid the premature problem of GA, the mutation process is replaced by a self-improving process to let the algorithm focus the operations on any potential solutions before discarding them in the selection process. The case studies show that the proposed GA-focus algorithm converges faster than RRT* and can be applied to various large-scale and challenging problems filled with obstacles of different shapes and sizes, and find high-quality solutions.
{"title":"Path Planning for Autonomous Systems Design: A Focus Genetic Algorithm for Complex Environments","authors":"Chuan Hu, Yan Jin","doi":"10.1115/1.4063013","DOIUrl":"https://doi.org/10.1115/1.4063013","url":null,"abstract":"\u0000 Path planning has been a hot research topic in robotics and is a vital functionality for autonomous systems. As the time complexity of traditional path planning algorithms grows rapidly with the complexity of the problem, evolutionary algorithms are widely applied for their near-optimal solutions. However, evolutionary algorithms can be trapped in a local optimum or converge to infeasible solutions, especially for large search spaces. As the problem scale increases, evolutionary algorithms often cannot find feasible solutions with random exploration, making it extremely challenging to solve long-range path-planning problems in environments with obstacles of various shapes and sizes. For long-range path planning of an autonomous ship, the current downsampling map approach may result in the disappearance of rivers and make the problem unsolvable. This paper introduces a novel area-based collision assessment method for Genetic Algorithm (GA) that can always converge to feasible solutions with various waypoints in large-scale and obstacle-filled environments. Waypoint-based crossover and mutation operators are developed to allow GA to modify the length of the solution during planning. To avoid the premature problem of GA, the mutation process is replaced by a self-improving process to let the algorithm focus the operations on any potential solutions before discarding them in the selection process. The case studies show that the proposed GA-focus algorithm converges faster than RRT* and can be applied to various large-scale and challenging problems filled with obstacles of different shapes and sizes, and find high-quality solutions.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130324648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vinod P. Gehlot, Mark Balas, M. Quadrelli, Saptarshi Bandyopadhyay, D. Bayard, A. Rahmani
Quantum Computing and Quantum Information Science is a burgeoning engineering field at the cusp of solving challenging robotic applications. This paper introduces a hybrid (gate-based) quantum computing and classical computing architecture to solve the motion propagation problem for a robotic system. This paper presents the quantum-classical architecture for linear differential equations defined by two types of linear operators: Unitary and Non-Unitary system matrices, thereby solving any linear ordinary differential equation. The ability to encode information using bits - or qubits - is essential in any computation problem. The results in this paper also introduce two novel approaches to encoding any arbitrary state vector or any arbitrary linear operator using qubits. Unlike other algorithms that solve ODEs using purely quantum or classical architectures, the ODE solver presented in this paper leverages the best of quantum and classical computing paradigms.
{"title":"A Path to Solving Robotic Differential Equations Using Quantum Computing","authors":"Vinod P. Gehlot, Mark Balas, M. Quadrelli, Saptarshi Bandyopadhyay, D. Bayard, A. Rahmani","doi":"10.1115/1.4062615","DOIUrl":"https://doi.org/10.1115/1.4062615","url":null,"abstract":"\u0000 Quantum Computing and Quantum Information Science is a burgeoning engineering field at the cusp of solving challenging robotic applications. This paper introduces a hybrid (gate-based) quantum computing and classical computing architecture to solve the motion propagation problem for a robotic system. This paper presents the quantum-classical architecture for linear differential equations defined by two types of linear operators: Unitary and Non-Unitary system matrices, thereby solving any linear ordinary differential equation. The ability to encode information using bits - or qubits - is essential in any computation problem. The results in this paper also introduce two novel approaches to encoding any arbitrary state vector or any arbitrary linear operator using qubits. Unlike other algorithms that solve ODEs using purely quantum or classical architectures, the ODE solver presented in this paper leverages the best of quantum and classical computing paradigms.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130203455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tyler Ard, B. Pattel, Karla Fuhs, A. Vahidi, H. Borhan
Truck platooning closely regulates gaps between heavy duty freight trucks to exploit slipstream effects for reducing aerodynamic friction - and therefore reducing engine effort and fuel usage. Currently deployed applications of this have been classically actuated through error-correcting PID feedback loops with connectivity amongst trucks in a fleet to form a connected and adaptive cruise control law that attenuates disturbances between trucks to maintain tolerable gaps. Typically, performance of such systems is challenged by difficult, albeit not uncommon, transients when under traffic conditions and when under road grade variations. Because of this, such platooning control requires attentive and trained drivers to disengage the adaptive cruise control - which limits its potentials for reducing driver load. More advanced longitudinal motion planning under predictive optimal control can push for higher levels of autonomy under a larger range of scenarios, as well as improve fuel efficiency. Here, model predictive control for fuel-performant truck platooning is vetted in both simulation and experimentation for representative traffic and road-grade routes. Several approaches are used exploiting physics-based models with and without the powertrain system, and neural network-encoded models. The fuel benefits of aerodynamic platooning are isolated from the more general eco-driving approach, which already provides fuel benefit to trucks by smartly selecting truck velocity. Results from simulation and validation in experimentation are presented - showing up to 6% benefit in fuel economy through eco-driving and an additional 3% achievable through platooning. Observed losses in fuel performance are explained by energy dissipation from braking.
{"title":"Simulated and Experimental Verification of Fuel Efficient Truck Platooning with Model Predictive Control Under Grade and Traffic Disturbances","authors":"Tyler Ard, B. Pattel, Karla Fuhs, A. Vahidi, H. Borhan","doi":"10.1115/1.4062532","DOIUrl":"https://doi.org/10.1115/1.4062532","url":null,"abstract":"\u0000 Truck platooning closely regulates gaps between heavy duty freight trucks to exploit slipstream effects for reducing aerodynamic friction - and therefore reducing engine effort and fuel usage. Currently deployed applications of this have been classically actuated through error-correcting PID feedback loops with connectivity amongst trucks in a fleet to form a connected and adaptive cruise control law that attenuates disturbances between trucks to maintain tolerable gaps. Typically, performance of such systems is challenged by difficult, albeit not uncommon, transients when under traffic conditions and when under road grade variations. Because of this, such platooning control requires attentive and trained drivers to disengage the adaptive cruise control - which limits its potentials for reducing driver load. More advanced longitudinal motion planning under predictive optimal control can push for higher levels of autonomy under a larger range of scenarios, as well as improve fuel efficiency. Here, model predictive control for fuel-performant truck platooning is vetted in both simulation and experimentation for representative traffic and road-grade routes. Several approaches are used exploiting physics-based models with and without the powertrain system, and neural network-encoded models. The fuel benefits of aerodynamic platooning are isolated from the more general eco-driving approach, which already provides fuel benefit to trucks by smartly selecting truck velocity. Results from simulation and validation in experimentation are presented - showing up to 6% benefit in fuel economy through eco-driving and an additional 3% achievable through platooning. Observed losses in fuel performance are explained by energy dissipation from braking.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126716184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-point path planning problem is a classic problem of the mobile robot. The present research is concerned with solving the shortest path. In some real applications, the shortest path length is not always the significant concerned value of path planning. This article proposes an extended generalized cost concept to constitute the updated path planning strategy. The generalized costs are the combination of straightly moving and turning costs. A genetic algorithm is used to solve the optimal path planning problems. The different weight between the two kinds of costs and how the different parameters affect the optimal path solution is discussed. The generalized cost concept extends the application of path planning to diversified physical quantities. When estimating the composite optimal costs of the path planning problem, we only need to solve the path planning problem with simplex straightly moving costs or simplex turning costs.
{"title":"Multi-point path planning algorithm for a mobile robot with composite moving costs","authors":"Junjie Ji, Jing-Shan Zhao","doi":"10.1115/1.4056759","DOIUrl":"https://doi.org/10.1115/1.4056759","url":null,"abstract":"\u0000 Multi-point path planning problem is a classic problem of the mobile robot. The present research is concerned with solving the shortest path. In some real applications, the shortest path length is not always the significant concerned value of path planning. This article proposes an extended generalized cost concept to constitute the updated path planning strategy. The generalized costs are the combination of straightly moving and turning costs. A genetic algorithm is used to solve the optimal path planning problems. The different weight between the two kinds of costs and how the different parameters affect the optimal path solution is discussed. The generalized cost concept extends the application of path planning to diversified physical quantities. When estimating the composite optimal costs of the path planning problem, we only need to solve the path planning problem with simplex straightly moving costs or simplex turning costs.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128750183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new method of detecting incipient immobilization for a wheeled mobile robot operating in deformable terrain with high spatial variability. This approach uses proprioceptive sensor data from a four-wheeled, rigid chassis rover operating in poorly bonded, compressible snow to develop canonic, dynamical system models of the robot's operation. These serve as hypotheses in a multiple model estimation algorithm used to predict the robot's mobility in real-time. This prediction method eliminates the need for choosing an empirical wheel-terrain interaction model, determining terramechanics parameter values, or for collecting large training datasets needed for machine learning classification. When tested on field data, this new method warns of decreased mobility an average of 1.8 meters and 2.9 seconds before the rover is completely immobilized. This system also proves to be a reliable predictor of immobilization when evaluated in simulated scenarios of rovers with passive suspension maneuvering in more variable terrain.
{"title":"Incipient Immobilization Detection for Lightweight Rovers Operating in Deformable Terrain","authors":"A. Lines, Joshua Elliott, L. Ray","doi":"10.1115/1.4056408","DOIUrl":"https://doi.org/10.1115/1.4056408","url":null,"abstract":"\u0000 This paper presents a new method of detecting incipient immobilization for a wheeled mobile robot operating in deformable terrain with high spatial variability. This approach uses proprioceptive sensor data from a four-wheeled, rigid chassis rover operating in poorly bonded, compressible snow to develop canonic, dynamical system models of the robot's operation. These serve as hypotheses in a multiple model estimation algorithm used to predict the robot's mobility in real-time. This prediction method eliminates the need for choosing an empirical wheel-terrain interaction model, determining terramechanics parameter values, or for collecting large training datasets needed for machine learning classification. When tested on field data, this new method warns of decreased mobility an average of 1.8 meters and 2.9 seconds before the rover is completely immobilized. This system also proves to be a reliable predictor of immobilization when evaluated in simulated scenarios of rovers with passive suspension maneuvering in more variable terrain.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127143868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Petty, J. Fernández, Jason Fischell, Luis A De Jesus-Diaz
Off-road autonomous vehicles face a unique set of challenges compared to those designed for road use. Lane markings and road signs are unavailable, with soft soils, mud, steep slopes, and vegetation taking their place. Autonomy struggles with shrubbery, saplings, and tall grasses. It can be difficult to determine if this vegetation or what it obscures is drivable. Modeling and simulation of autonomy sensors and the environments they interact with enhances and accelerates autonomy development, but analytical models found in the literature and our in-house simulation software did not agree on how well lidar penetrates grass-like vegetation. To test our simulator against the analytical model, we constructed vegetation mock-ups that conform to the assumptions of the analytical model and measured the pass-through rate on calibrated lidar targets. Vegetation density, lidar-to-vegetation distance, and target reflectivity were varied. A random effects model was used to address the dependence introduced by repeated measures, which increased accuracy while reducing time and cost. Stem density impacted total beam return count and grass patch pass-through rate. Target reflectivity results varied by lidar unit, and three-way factor interaction was significant. Results suggest benchmarking experiments could be useful in autonomy development.
{"title":"Lidar Attenuation Through a Physical Model of Grass-like Vegetation","authors":"T. Petty, J. Fernández, Jason Fischell, Luis A De Jesus-Diaz","doi":"10.1115/1.4055944","DOIUrl":"https://doi.org/10.1115/1.4055944","url":null,"abstract":"\u0000 Off-road autonomous vehicles face a unique set of challenges compared to those designed for road use. Lane markings and road signs are unavailable, with soft soils, mud, steep slopes, and vegetation taking their place. Autonomy struggles with shrubbery, saplings, and tall grasses. It can be difficult to determine if this vegetation or what it obscures is drivable. Modeling and simulation of autonomy sensors and the environments they interact with enhances and accelerates autonomy development, but analytical models found in the literature and our in-house simulation software did not agree on how well lidar penetrates grass-like vegetation. To test our simulator against the analytical model, we constructed vegetation mock-ups that conform to the assumptions of the analytical model and measured the pass-through rate on calibrated lidar targets. Vegetation density, lidar-to-vegetation distance, and target reflectivity were varied. A random effects model was used to address the dependence introduced by repeated measures, which increased accuracy while reducing time and cost. Stem density impacted total beam return count and grass patch pass-through rate. Target reflectivity results varied by lidar unit, and three-way factor interaction was significant. Results suggest benchmarking experiments could be useful in autonomy development.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116678185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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