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Research on Trajectory Planning for Four-wheel Steering Autonomous Vehicle with V2V Communication

Jilin University-Fangwu Ma, Yucheng Shen, Jiahong Nie, Xiyu Li, Yu Yang, Jiawei WANG, Guanpu Wu
  • Technical Paper
  • 2020-01-0114
To be published on 2020-04-14 by SAE International in United States
Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development and application of connected autonomous vehicles with V2V communication provides more advanced control strategies to research of lane-changing. Meanwhile, Four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Ackerman front-wheel steering with proportional rear-wheel steering vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles. Then, the polynomial function and Sin function were used for the lane-changing trajectory planning and the extended rectangular vehicle model was established to get vehicle collision avoidance condition. Vehicle comfort requirements and lane-changing efficiency were used as the optimization variables of optimization function.…
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Energy Efficient Maneuvering of Connected and Automated Vehicles

Southwest Research Insitute-Michael Gross
Southwest Research Institute-Sankar Rengarajan, Scott Hotz, Jayant Sarlashkar, Stanislav Gankov, Piyush Bhagdikar, Charles Hirsch
  • Technical Paper
  • 2020-01-0583
To be published on 2020-04-14 by SAE International in United States
Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies will allow a vehicle to "know" its future operating conditions with some degree of certainty, greatly narrowing prior information gaps. The increased development of such Connected and Automated Vehicle (CAV) systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles. The NEXTCAR program is one such initiative by the Advanced Research Projects Agency – Energy (ARPA-E) to developed advanced vehicle dynamics and powertrain control technologies that leverage such connected information streams. Southwest Research Institute (SwRI) in collaboration with Toyota and University of Michigan is currently working on improving energy consumption of a Toyota Prius Prime 2017 by 20%. This paper provides an overview of the various algorithms that have been developed to achieve the energy consumption target. A breakdown of how individual algorithms contribute to the overall target is presented. The team built a specialized test-bed called CAV dynamometer that integrates a traffic simulator and a hub dynamometer for testing the…
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Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window.

CEAS Western Michigan University-Alvis Fong
Colorado State Univ-Thomas Bradley, Lowell Hanson
  • Technical Paper
  • 2020-01-0729
To be published on 2020-04-14 by SAE International in United States
Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide the highest velocity prediction fidelity. We developed LSTM deep neural network which uses different groups of datasets collected in Fort Collins. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. Effect of different group of datasets on forward velocity prediction window of 10, 15, 20 and 30 seconds is studied. Developed algorithm is tested…
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Methods for quantifying the impact severity of low-speed side impacts at varying angles

American Bio Engineers-Justin Brink, Scott Swinford, Brian Jones
Biomechanical Research & Testing-Christopher Furbish, Judson Welcher
  • Technical Paper
  • 2020-01-0641
To be published on 2020-04-14 by SAE International in United States
Accurately quantifying the severity of minor vehicle-to-vehicle impacts has commonly been achieved by utilizing the Momentum Energy Restitution (MER) method. A review of the scientific literature revealed investigations assessing the efficacy of the MER method primarily for: 1) inline rear-end impacts, 2) offset rear-end impacts, and 3) side impacts configured with the bullet vehicle striking the target vehicle at an approximate 90° angle. To date, the utility of the MER method has not been examined and readily published for quantifying oblique side impacts. The aim of the current study was to observe the effectiveness of the MER method for predicting the severity of side impacts at varying angles. Data were collected over a sequence of 12 tests with bullet-to-target-vehicle contact angles ranging from approximately 45° to 135° with corresponding impact speeds of approximately 13.7 km/h to 16.4 km/h. Vehicle damage profiles documented after each test allowed for the application of the MER method to calculate the target vehicle’s change in velocity (ΔV). Calculated ΔV’s were then compared to the vehicle’s recorded change in velocity obtained…
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Hardware-in-the-Loop and Public Road Testing of RLVW and GLOSA Connected Vehicle Applications

Camp LLC-Jayendra Parikh
Ford Motor Co Ltd-Gopichandra Surnilla, Alexander Katriniok
  • Technical Paper
  • 2020-01-1379
To be published on 2020-04-14 by SAE International in United States
Each year, large number of traffic accidents with a large number of injuries and fatalities occur. To reduce these accidents, automotive companies have been developing newer and better active and passive safety measures to increase the safety of passengers. With the developments in connected vehicle infrastructure on the roads and on-board-units for Vehicle to Everything (V2X) connectivity in newer vehicles, V2X communication offers possibilities for preventing accidents as V2X equipped vehicles have situational awareness of other vehicles and road users around them through Vehicle to Vehicle (V2V) and Vehicle to Pedestrian (V2P) communication, and signal phase and timing and map information on signalized intersections through Vehicle to Infrastructure (V2I) communication. Therefore, vehicle on-board computers can calculate an optimal speed profile for fuel economy purposes or prevent crashes related to red light violations. This paper addresses these two main advantages, firstly by developing and using Hardware-in-the-Loop (HIL) simulator testing and experimental vehicle testing environments of an algorithm for preventing red light violation, called Red Light Violation Warning (RLVW). The HIL simulator used in the testing is…
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Energy-optimal deceleration planning system for regenerative braking of electrified vehicles with connectivity and automation

Hyundai Motor Co & KIA Motors Corp-Jeong Soo Eo
Hyundai Motor Company-Dohee Kim
  • Technical Paper
  • 2020-01-0582
To be published on 2020-04-14 by SAE International in United States
This paper presents an energy-optimal deceleration planning system (EDPS) to maximize regenerative energy for electrified vehicles on deceleration events resulted from map information and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while considering vehicles driving in front of ego-vehicle. The EDPS is an ecological driver assistance system with level 2 or 3 automation since acceleration is operated by an adaptive cruising system or a human driver and deceleration is operated on a unit of deceleration events which are divided into static ones such as turning and warning as well as dynamic ones such as traffic light. The event-based optimal deceleration profile is obtained by a dynamic programming framework including a driving motor performance model and a gear box model, and with the detection of a front vehicle the profile is updated in real time by nonlinear model predictive control scheme which considers a connected configuration and a modified intelligent driver model. The performance of EDPS has been rigorously validated both based on real-world…
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Scalable decentralized solution for secure Vehicle-to-Vehicle communication

Tata Elxsi Ltd-Rajesh Koduri, Sivaprasad Nandyala, Mithun Manalikandy
Tata Elxsi, Ltd.-Sreelakshmi S. Vattaparambil
  • Technical Paper
  • 2020-01-0724
To be published on 2020-04-14 by SAE International in United States
The automotive industry is set for a rapid transformation in the next few years in terms of communication. The kind of growth the automotive industry is poised for in fields of connected cars is both fascinating and alarming at the same time. The communication devices equipped to the cars and the data exchanges done between vehicle to a vehicle are prone to a lot of cyber-related attacks. The signals that are sent using Vehicular Adhoc Network (VANET) between vehicles can be eavesdropped by the attackers and it may be used for various attacks such as the man in the middle attack, DOS attack and Sybil attack. These attacks can be prevented using the Blockchain technology, where each transaction are logged in a decentralized immutable Blockchain ledger. This provides authenticity and integrity to the signals. But the use of Blockchain Platforms such as Ethereum has various drawbacks like scalability which makes it infeasible for connected car system. In this paper, we propose a solution to address various drawbacks of VANET such as privacy issues and, security…
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Reinforcement Learning based Energy Management of Plug-in Hybrid Electric Vehicles for Commuter Route

Univ of Minnesota-Twin Cities-Pengyue Wang, William Northrop
  • Technical Paper
  • 2020-01-1189
To be published on 2020-04-14 by SAE International in United States
Optimization-based (OB) methods used in vehicle energy management strategies (EMS) have potential to significantly increase fuel economy and extend the electric-only range of plug-in hybrid electric vehicles (PHEVs). However, it is difficult to apply OB methods in real-world vehicles as they require accurate detailed and high-resolution information about the future including second-by-second vehicle velocity profile data, to optimize energy on upcoming routes. This information can be potentially provided by techniques like Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) connections, and may be more availability with the advent of vehicle automation. However, considerable uncertainty in actual traffic, driver behavior, and road conditions renders prediction of future trajectories difficult, if not possible. Therefore, OB methods are difficult to apply to current real-world vehicles. In this paper, a practical reinforcement learning (RL) algorithm for automatic mode-switching of a multimode PHEV is introduced. The PHEV used in the work was a Chevrolet Volt driven on a simulated commuter route. The goal if the RB method is to blend the charge depleting (CD) and charge sustaining (CS) modes during the trip to…
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Leverage wireless technologies in timber harvesting to enhance operational productivity and business profitability

John Deere-ishani pandit, Suchitra Iyer
  • Technical Paper
  • 2020-01-1378
To be published on 2020-04-14 by SAE International in United States
Growing needs of forestry products; primarily wood followed by pulp and paper industry have mechanized the process of harvesting timber in most part of world. Such job sites have several machines and vehicles working together to harvest and transport the logs. Timber logging is very similar to crop harvesting with longer harvesting cycle and hence it is critical that every part of it is effectively utilized; timely harvest and transport to factories play an important role. Traditionally, these areas have had little cellular connectivity, restricting communication between operators, machines, land owners and factories. With better connectivity, it will be easier to monitor and operate job sites for example if skidder would know how many trees are felled, how many logs and bunches are created and where they are kept; it would reduce time and fuel spent in searching for logs. Also, with better communication between machines, skidder would know when to pick up logs and avoid longer wait time. Timely pick up of felled trees is critical in ensuring log quality. With upcoming wireless technologies…
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How battery technology will drive truck electrification

SAE Truck & Off-Highway Engineering: December 2019

Alexander Schey
  • Magazine Article
  • 19TOFHP12_10
Published 2019-12-01 by SAE International in United States

The past three years have seen a major shift in the perception around electrified commercial vehicles, including trucks, driven by a variety of factors that have come together at this particular time. These factors include a growing awareness and acceptance of the impact of CO2 emissions on climate change and the dangers of diesel emissions-most notably highlighted by the Volkswagen emissions scandal-alongside a growing maturity and improved cost profile on electric vehicle (EV) technology. As a result, fleet owners and OEMs now consider e-trucks much more seriously.

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