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An ADAS Feature Rating System: Proposing a New Industry Standard

Velodyne LiDAR-David Heeren, Mircea Gradu
  • Technical Paper
  • 2019-24-0251
To be published on 2019-10-07 by SAE International in United States
The rapid introduction of Advanced Driver-Assistance Systems (ADAS) in modern vehicles has the commendable overarching goal of improving the Safety of the driver, passengers and other traffic participants. As integral part of Automated Vehicles included within the SAE Levels 1-3, ADAS prepare the path toward full autonomy and consequently they are subjected to some of the same challenges. A recent SAE EDGE™ report considers the following four areas to be unsettled domains in automated vehicle sensors, due mainly to the lack of common understanding around various aspects pertaining to each of them: • Terminology and taxonomy • Testing, simulation, and calibration • Security, robustness, and integrity • Data ownership and privacy Velodyne LiDAR, Inc. is the lead company in automotive Lidar sensing technology, but also one of the strongest Safety Advocacy voices within the industry. Accordingly, Velodyne published several thought leading white papers addressing the first three topics listed above. This new SAE paper by Velodyne will build on the previous framework related to developing, testing, validating and marketing Lidar centric Safety oriented ADAS solutions.…

The Sense-itive Side of Autonomous Vehicles

Autonomous Vehicle Engineering: July 2019

Kami Buchholz
  • Magazine Article
  • 19AVEP07_07
Published 2019-07-01 by SAE International in United States

BASF is exploring how specific materials-and even paint colors and finishes-can improve the capabilities of AV sensors.

The sensing technologies needed for automated-driving vehicles are evolving as the industry moves toward high-level (SAE Level 4-5) automation. Sophisticated sensors already enable advanced driver-assistance systems (ADAS) features such as adaptive cruise control, park assist, lane-centering and others.

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Energy-Efficient Cooperative Adaptive Cruise Control with Receding Horizon of Traffic, Route Topology, and Traffic Light Information

SAE International Journal of Connected and Automated Vehicles

AVL List GmbH, Austria-Alejandro Ferreira Parrilla, Stephen John Jones
Chalmers University of Technology, Sweden-Anders Grauers
  • Journal Article
  • 12-02-02-0006
Published 2019-05-16 by SAE International in United States
Advanced and cooperative vehicle (semi-) autonomous driving systems will become a necessity in the future for sustainable, convenient, and safe mobility. By utilizing Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, a vehicle’s energy consumption can be reduced while maintaining safety and driving comfort. A holistic control strategy is presented, which in a novel way incorporates traffic lights, road speed limits, gradients, and curvature, as well as surrounding traffic and detailed powertrain characteristics into a single Model Predictive Control formulation. The performance of the system is evaluated using a realistic co-simulation toolchain representing the vehicle, driver, and road, including complex traffic conditions. The approach is valid for a wide range of scenarios, ranging from urban city driving to highways. Simulation results for a D-class passenger car with a diesel engine and an automatic transmission in an urban route show energy savings between 5% and 30% with an unchanged travel time, compared to a simulated human driver.

An Immersive Vehicle-in-the-Loop VR Platform for Evaluating Human-to-Autonomous Vehicle Interactions

Clemson University-Roberto Merco, Manveen Kaur, Anjan Rayamajhi, Gianluca Papa, Pierluigi Pisu, Sabarish Babu, Andrew Robb, Jim Martin
Maserati-Marco Gavelli
Published 2019-04-02 by SAE International in United States
The deployment of autonomous vehicles in real-world scenarios requires thorough testing to ensure sufficient safety levels. Driving simulators have proven to be useful testbeds for assisted and autonomous driving functionalities but may fail to capture all the nuances of real-world conditions. In this paper, we present a snapshot of the design and evaluation using a Cooperative Adaptive Cruise Control application of virtual reality platform currently in development at our institution. The platform is designed so to: allow for incorporating live real-world driving data into the simulation, enabling Vehicle-in-the-Loop testing of autonomous driving behaviors and providing us with a useful mean to evaluate the human factor in the autonomous vehicle context.
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Analysis of Driver’s Behavior under Following-Go Scenario

Tongji University, Shanghai, China-Lan Xia, Xichan Zhu, Zhixiong Ma
Published 2019-04-02 by SAE International in United States
The driver’s behavior under following-go scenario, which has been involved in little research so far, is an important part of the driver's following behavior. Analysis of driver's behaviour under following-go scenario is important for improving the performance and the adaptability of ACC (Adaptive Cruise Control) systems in urban traffic environment. In this paper driver’s behavior under following-go scenario in real traffic is studied based on naturalistic driving data. Starting reaction time and starting distance from the target vehicle are used to evaluate driver’s starting timing under following-go scenario. Starting acceleration is used to evaluate the effect of driver’s acceleration operation under following-go scenario. The naturalistic driving data collected in china is screened and classified and the following-go scenario is obtained. The driver’s behaviour parameters under following-go scenario are extracted and the statistical characteristics are obtained. Influence factors are analyzed with univariate ANOVA (Analysis of Variance) and regression analysis. The results show that the starting reaction time and the starting distance from the target vehicle approximately obey the lognormal probability distribution and the starting acceleration approximately…
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Cooperative Adaptive Cruise Control Design and Implementation

Ohio State University-Mustafa Ridvan Cantas, Sukru Yaren Gelbal, Levent Guvenc, Bilin Aksun Guvenc
Published 2019-04-02 by SAE International in United States
In this manuscript a design and implementation of CACC on an autonomous vehicle platform (2017 Ford Fusion) is presented. The developed CACC controls the intervehicle distance between the target vehicle and ego vehicle using a feedforward PD controller. In this design the feedforward information is the acceleration of the target vehicle which is communicated through Dedicated Short-Range Communication (DSRC) modem. The manuscript explains the detailed architecture of the designed CACC with used hardware and methods for the both simulation and experiments. Also, an approach to overcome detection failures at the curved roads is presented to improve overall quality of the designed CACC system. As a result, the initial simulation and experimental results with the designed CACC system is presented in the paper. The presented results indicate that CACC improves the car following performance of the ego vehicle as compared to the classical Adaptive Cruise Controller.
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A Novel Approach for Validating Adaptive Cruise Control (ACC) Using Two Hardware-in-the-Loop (HIL) Simulation Benches

Ford Motor Co., Ltd.-Adit Joshi
Published 2019-04-02 by SAE International in United States
Adaptive Cruise Control (ACC) is becoming a common feature in modern day vehicles with the advancement of Advanced Driver Assist Systems (ADAS). Simultaneously, Hardware-in-the-Loop (HIL) simulation has emerged as a major component of the automotive product development cycle as it can accelerate product development and validation by supplementing in-vehicle testing. Specifically, HIL simulation has become an integral part of the controls development and validation V-cycles by enabling rapid prototyping of control software for Electronic Control Units (ECUs). Traditionally, ACC algorithms have been validated on a system or subsystem HIL bench with the ACC ECU in the loop such that the HIL bench acts as the host or trailing vehicle with the target or preceding vehicle usually simulated using as an object that follows a pre-defined motion profile. In this setup, the host vehicle HIL bench generally includes physical components and subsystems or their corresponding simulated representations with varying degrees of fidelity. However, the simulated target vehicle is typically used as a low fidelity object for which the motion is described only as functions of lateral…
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Determining the Greenhouse Gas Emissions Benefit of an Adaptive Cruise Control System Using Real-World Driving Data

General Motors LLC-William Dvorkin, Joshua King, Marc Gray, Shyhyeu Jao
Published 2019-04-02 by SAE International in United States
Adaptive cruise control is an advanced vehicle technology that is unique in its ability to govern vehicle behavior for extended periods of distance and time. As opposed to standard cruise control, adaptive cruise control can remain active through moderate to heavy traffic congestion, and can more effectively reduce greenhouse gas emissions. Its ability to reduce greenhouse gas emissions is derived primarily from two physical phenomena: platooning and controlled acceleration. Platooning refers to reductions in aerodynamic drag resulting from opportunistic following distances from the vehicle ahead, and controlled acceleration refers to the ability of adaptive cruise control to accelerate the vehicle in an energy efficient manner. This research calculates the measured greenhouse gas emissions benefit of adaptive cruise control on a fleet of 51 vehicles over 62 days and 199,300 miles. To our knowledge, the greenhouse gas emissions benefit of an advanced vehicle technology has never been demonstrated in this manner, and no automaker has published such extensive data pertaining to adaptive cruise control. These results highlight the opportunity to further reduce consumer fuel use and…
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Smart DPF Regenerations - A Case Study of a Connected Powertrain Function

Ford Motor Company-Michael Hopka, Devesh Upadhyay, Michiel Van Nieuwstadt
Published 2019-04-02 by SAE International in United States
The availability of connectivity and autonomy enabled resources, within the automotive sector, has primarily been considered for driver assist technologies and for extending the levels of vehicle autonomy. It is not a stretch to imagine that the additional information, available from connectivity and autonomy, may also be useful in further improving powertrain functions. Critical powertrain subsystems that must operate with limited or uncertain knowledge of their environment stand to benefit from such new information sources. Unfortunately, the adoption of this new information resource has been slow within the powertrain community and has typically been limited to the obvious problem choices such as battery charge management for electric vehicles and efforts related to fuel economy benefits from adaptive/coordinated cruise control. In this paper we discuss the application of connectivity resources in the management of an aftertreatment sub-system, the Diesel Particulate Filter (DPF). Standard DPF regenerations are scheduled on an inferred soot load based on indirect indicators of system state, such as exhaust gas flow rate and pressure drop across the DPF and/or empirical models of engine…
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Computationally Efficient Reduced-Order Powertrain Model of a Multi-Mode Plug-In Hybrid Electric Vehicle for Connected and Automated Vehicles

Michigan Technological University-Neeraj Rama, Darrell Robinette
Published 2019-04-02 by SAE International in United States
This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources. Although overall estimation accuracy is less than neural network and high-fidelity models, emphasis on runtime minimization with reasonable estimation accuracy enables energy optimization of CAVs without a need for computationally expensive server-based models. Performance of the model is evaluated on a fleet of second-generation Chevrolet Volts in a variety of driving scenarios and drive cycle durations. On-road testing indicates that the model can estimate actual vehicle…
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