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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 Institute-Sankar Rengarajan, Scott Hotz, Jayant Sarlashkar, Stanislav Gankov, Piyush Bhagdikar, Michael C. Gross, 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.

Colorado State University-Aaron Rabinowitz, Thomas Bradley
Western Michigan University-Tushar Gaikwad, Farhang Motallebiaraghi, Zachary Asher, Alvis Fong, Rick Meyer
  • 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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Utilization of vehicle connectivity for improved energy consumption of a speed harmonized cohort of vehicles.

Michigan Technological University-Christopher Morgan, Darrell Robinette, Pruthwiraj Santhosh, John Bloom-Edmonds
  • Technical Paper
  • 2020-01-0587
To be published on 2020-04-14 by SAE International in United States
Improving vehicle response through advanced knowledge of traffic behavior can lead to large improvements in energy consumption for the single isolated vehicle. This energy savings across multiple vehicles can even be larger if they travel together as a cohort in harmonization. Additionally, if the vehicles have enough information about their immediate path of travel, and other vehicles’ in that path (and their respective critical forward looking information), they can safely drive close enough to each other to share aerodynamic load. These energy savings can be upwards of multiple percentage points, and are dependent on several criteria. This analysis looks at criteria that contributes to energy savings for a cohort of vehicles in synchronous motion, as well as describes a study that allows for better understanding of the potential benefits of different types of cohorted vehicles in different platoon arrangements. The basis of this study is a precursor to developing a connected vehicle application that safely allows for fully controlled platooning on open highway for multi-destination vehicles. In this study, two types of light duty passenger…
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Self-exploration of Non-holonomic Automated System Under Dynamic Environment

Isuzu Technical Center of America Inc.-Weiyang Zhang, Yong Sun, Haokun He, Wenbo Yu, Pengcheng Cai
  • Technical Paper
  • 2020-01-0126
To be published on 2020-04-14 by SAE International in United States
Exploring an unknown place autonomously is a challenge for robots, especially when the environment is changing. Moreover, in real world application, efficient path planning is crucial for the autonomous vehicles to have timely response to execute a collision-free motion. In this paper we focus on environment exploration which enables an automated system to establish a map of an unknown environment with dynamic objects moving within it. We introduce an exploration package that enables robot’s self-exploration with an online collision avoidance planner. The package consists of exploration module, global planner module and local planner module. We modularize the package so that developers can easily make modifications or even substitutions to some modules for their specific application. In order to validate the algorithm, we designed and built a robot car as a low cost validation platform to test the autonomous vehicle algorithms in the real world. The car has a 22.36 x 11.65 x 7.6 inches, 4X4 brushless short course truck chassis, which has a dynamic model similar to a passenger car, but in a scaled pattern.…
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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.-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.-Dohee Kim
Hyundai Motor Co. & KIA Motors Corp.-Jeong Soo Eo, Ryan Miller
  • 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.-Sreelakshmi S. Vattaparambil, Rajesh Koduri, Sivaprasad Nandyala, Mithun Manalikandy
  • 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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Using Vehicle Specifications to Gain Insights into Different Automotive Market Requirements

Mahindra & Mahindra, Ltd.-Lemuel Paulraj, Saravanan Muthiah
  • Technical Paper
  • 2020-01-1283
To be published on 2020-04-14 by SAE International in United States
Determination of vehicle specifications (for example, powertrain sizing) is one of the fundamental steps in any new vehicle development process. The vehicle system engineer needs to select an optimum combination of vehicle, engine and transmission characteristics based on the product requirements received from Product Planning (PP) and Marketing teams during concept phase of any vehicle program. This process is generally iterative and requires subject matter expertise. For example, accurate powertrain sizing is essential to meet the required fuel economy (FE), performance and emission targets for different vehicle configurations. This paper analyzes existing vehicle specifications (Passenger Cars/SUVs - Gasoline/Diesel) in different automotive markets (India, Europe, US, Japan) and aims to determine underlying trends across them. Scatter band analysis is carried out for specifications such as vehicle kerb weight (WT), vehicle length (L), vehicle width (W), vehicle height (H), footprint area (FPA), engine cubic capacity (CC) and engine power (P). CC/WT vs FE, CC/FPA vs FE, P/WT vs FE, FPA/(LXW), CC/(FPAXH), FPAXH and WXH trends are analyzed amongst others. It is interesting to note that similarities exist…
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Reinforcement Learning based Energy Management of Plug-in Hybrid Electric Vehicles for Commuter Route

University 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…