Browse Topic: Adaptive cruise control

Items (256)
ABSTRACT Connected and automated vehicles (CAVS) have the potential to improve fuel economy by changing the way vehicles are driven. Fuel economy can be improved through a wide range of technologies, many of which do not require Level 5 automation. One of the most promising technologies is a smart cruise control that uses a speed-matching algorithm to account for fuel economy. Accounting for fuel economy in the algorithm leads to different driving behavior than simply matching the driver-entered set speed. This paper describes how such a smart cruise control could be applied to a class 8 vehicle both in simulation and in the actual vehicle on a closed test track. It evaluates the algorithm and describes the correlation procedure used to calibrate the model using test data from the vehicle
Sharer, PhillipRousseau, AymericKarbowski, DominikShen, DaliangHeim, ScottGonyou, Kevin MarkRizzo, DeniseRagatz, AdamGonder, JeffProhaska, RobertSong, Jae
ABSTRACT The transportation industry annually travels more than 6 times as many miles as passenger vehicles [1]. The fuel cost associated with this represents 38% of the total marginal operating cost for this industry [8]. As a result, industry’s interest in applications of autonomy have grown. One application of this technology is Cooperative Adaptive Cruise Control (CACC) using Dedicated Short-Range Communications (DSRC). Auburn University outfitted four class 8 vehicles, two Peterbilt 579’s and two M915’s, with a basic hardware suite, and software library to enable level 1 autonomy. These algorithms were tested in controlled environments, such as the American Center for Mobility (ACM), and on public roads, such as highway 280 in Alabama, and Interstates 275/696 in Michigan. This paper reviews the results of these real-world tests and discusses the anomalies and failures that occurred during testing. Citation: Jacob Ward, Patrick Smith, Dan Pierce, David Bevly, Paul Richardson
Ward, JacobSmith, PatrickPierce, DanBevly, DavidRichardson, PaulLakshmanan, SridharArgyris, AthanasiosSmyth, BrandonAdam, CristianHeim, Scott
ABSTRACT The Army has identified an operational need for a Robotic Convoy capability for its tactical vehicle fleets. The Department of Defense (DoD), with a fleet of over several hundred thousand tactical vehicles, must identify an approach with supporting technology and supply base to procure and support a Robotic Convoy solution at the lowest possible cost. While cost is a key driver, the selected system approach must be proven and robust to ensure the safety of our soldiers and the supply chain. An effective approach is to integrate and adapt the advanced automotive technologies, components and suppliers currently delivering advanced safety technologies into the automotive market. These advanced automotive technologies merged with DoD robotics enhancements in tactical behaviors, autonomous driving, command & control and unmanned systems collaboration will advance the operational utility of robotic convoy application in manned and unmanned modes. Figure 1 Military Application The
Coplen, Christina E.Lane, Gerald R.
A fully instrumented Tesla Model 3 was used to collect thousands of hours of real-world automated driving data, encompassing both Autopilot and Full Self-Driving modes. This comprehensive dataset included vehicle operational parameters from the data busses, capturing details such as powertrain performance, energy consumption, and the control of advanced driver assistance systems (ADAS). Additionally, interactions with the surrounding traffic were recorded using a perception kit developed in-house equipped with LIDAR and a 360-degree camera system. We collected the data as part of a larger program to assess energy-efficient driving behavior of production connected and automated vehicles. One important aspect of characterizing the test vehicle is predicting its car-following behavior. Using both uncontrolled on-road tests and dedicated tests with a lead car performing set speed maneuvers, we tuned conventional adaptive cruise control (ACC) equations to fit the vehicle’s behavior. We
Duoba, MichaelVellamattathil Baby, TinuPulpeiro Gonzalez, JorgeHomChaudhuri, Baisravan
The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient automated navigation. The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S. Department of Energy, MathWorks, and General Motors, where each team received a 2019 Chevrolet
Chowduri, SuhritMidlam-Mohler, ShawnSingh, Karun Prateek
Robustness testing of Advanced Driver Assistance Systems (ADAS) features is a crucial step in ensuring the safety and reliability of these systems. ADAS features include technologies like adaptive cruise control, lateral and longitudinal controls, automatic emergency braking, and more. These systems rely on various sensors, cameras, radar, lidar, and software algorithms to function effectively. Robustness testing aims to identify potential vulnerabilities and weaknesses in these systems under different conditions, ensuring they can handle unexpected scenarios and maintain their performance. Mileage accumulation is one of the validation methods for achieving robustness. It involves subjecting the systems to a wide variety of real-world driving conditions and driving scenarios to ensure the reliability, safety, and effectiveness of the ADAS features. Following ISO 21448 (Safety of the intended functionality-SOTIF), known hazardous scenarios can be tested and validated through robustness
Almasri, HossamFan, Hsing-HuaMudunuri, Venkateswara Raju
Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model. Simulations were run to model and test the Eco-CACC algorithms with different lead vehicle driving behaviors
Kavas-Torris, OzgenurGuvenc, Levent
Platooning is a coordinated driving strategy by which following trucks are placed into the wake of leading vehicles. Doing this leads to two primary benefits. First, the vehicles following are shielded from aerodynamic drag by a “pulling” effect. Secondly, by placing vehicles behind the leading truck, the leading vehicles experience a “pushing” effect. The reduction in aerodynamic drag leads to reduced fuel usage and, consequently, reduced greenhouse gas emissions. To maximize these effects, the inter-vehicle distance, or headway, needs to be minimized. In current platooning strategy iterations, Coordinated Adaptive Cruise Control (CACC) is used to maintain close following distances. Many of these strategies utilize the fuel rate signal as a controller cost function parameter. By using fuel rate, current control strategies have limited applicability to non-conventional powertrains. Vehicle Specific Power (VSP) has shown promise as a metric by which the performance of such controllers
Bentley, JohnStegner, EvanBevly, David M.Hoffman, Mark
Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system. And the input penalty factor of the adaptive cruise control system is designed as a
Yan, YangXin, YafeiZheng, Hongyu
Starting in 2021 Ducati introduced a radar based adaptive cruise control (ACC) developed by Bosch. It utilizes a single radar unit on the front of the motorcycle to detect the presence of vehicles ahead, as well as the separation distance. The system is not an automatic emergency braking (AEB) system but does have similar features. The Ducati ACC system does have limitations, some of which are explored in the subject research. Initial testing was conducted to document the engine braking in each gear. Following initial testing, several tests were performed at high closing speeds of over 100 kph. It was determined that at a closing speed of approximately 100 kph the ACC system would not react to a moving vehicle ahead. Additionally, the system will not react to a stopped vehicle or a “swerve-around” stopped vehicle that suddenly appears. Another series of tests were performed while actively following a vehicle at various speeds, with the front vehicle suddenly slowing to a stop and
Fatzinger, Edward C.Gonzaga, William
Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time. The simulation results show that the resilient controller outperforms the traditional controller in terms of maintaining a safe distance, staying below the speed limit, and the accuracy of the FDI estimation
Alnaser, Ala JamilHolland, JamesSargolzaei, Arman
Letter from the Focus Issue Editors
Song, ZiyouFeng, ShuoWu, GuoyuanLi, Zhaojian
Heavy vehicles are major fuel consumers in road transportation, and the traditional way to reduce fuel consumption is to reduce weight, resistance, improve mechanical transmission efficiency, and improve engine thermal efficiency. However, European heavy-duty truck companies took the lead in realizing predictive cruise control (PCC) technology on the basis of cruise through intelligent network technology, based on ADAS maps, and achieved good fuel saving effects. In this paper, by studying the fuel consumption characteristics of trucks, designing the dynamic parameters of the load and whole vehicle, the predictive adaptive cruise control (PACC) technology is realized based on the predictive cruise strategy, and the statistics of fuel saving rate under different cruise ratio conditions are analyzed through the big data platform
Qian, GuopingLu, ZhenghuaTian, JuntaoLiu, LianfangXi, ChongZhou, Xiaoying
This article presents a merge-aware cruise control method that incorporates vehicle-to-vehicle (V2V) information and aims at improving the energy efficiency of vehicles and reducing speed disruptions of merging traffic during highway merges. During the events of highway merges, the gap between the ego and the preceding vehicle reduces drastically, which can result in sudden braking of the ego vehicle and thus reduction of its energy efficiency. We propose a rather simple cruise control algorithm to eliminate such sudden variations in the gap and velocity with respect to the preceding vehicle during highway merges, thus reducing the large accelerations and braking during such events and thereby improving energy efficiency. The proposed algorithm incorporates future traffic information and has computational requirements similar to adaptive cruise control methods, hence it is real-time applicable. Data used in this article are taken from on-road experiments using a 2020 Tesla Model 3
Vellamattathil Baby, TinuHomChaudhuri , Baisravan
The closet in-path vehicle (CIPV) is recognized relying on the detection results for road lane lines in most current ACC system, which may not work well in the poor conditions, for example, unclear road lane lines, low light level, bad weather, and so on. To solve this problem, the article proposes a sensor fusion-based CIPV recognition algorithm independent of road lane lines. First, a robust Kalman filter based on the global coordinate system is designed to fuse the millimeter-wave radar and camera targets. The fusion algorithm can dynamically adjust the covariance matrix of sensor observations to avoid the influence of anomalous observations on the fusion results. Stable detection of targets by the fusion algorithm is the basis of the CIPV recognition algorithm. Then, the CIPV recognition algorithm generates virtual lane lines using the motion parameters of self-vehicle or the driving trajectory of vehicle target and develops a mode switch strategy for virtual lane lines generation
Yang, YifeiZhao, ZhiguoYu, QinDeng, YunhongLi, Wenchang
Startups are famous for moving quickly. Vinfast may want to slow things down. It was only 2019 when the Vietnamese company built its first cars, rebodied versions of gasoline BMWs that became hits in its home market. Vinfast speedily developed four electric SUVs, including the inaugural VF8 that SAE Media drove in southern California. At the same time, a cargo ship docked near San Francisco, carrying nearly 2,000 VF8s for customers in California and Canada. The next day, Vinfast announced plans to go public via a SPAC merger. And Vinfast recently broke ground on a $4 billion factory in North Carolina, targeting 150,000 units of annual capacity and more than 7,000 jobs
Ulrich, Lawrence
This works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called ego-vehicle with other vehicles and infrastructure using V2V and/or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them. In this work, a real-time ACC for a Battery Electric Vehicle, based on a Deep
Borneo, AngeloMiretti, FedericoAcquarone, MatteoMisul, Daniela
Steady advances in autonomous vehicle development are expected to lead to improved traffic flow in terms of string stability compared with that for human-driven vehicles. Fluctuation in intervehicle distances among a group of vehicles without string stability is amplified as it propagates upstream (rearward), which may cause traffic congestion. Since it will take a few decades for autonomous vehicles to replace all human-driven vehicles, it is important to tackle the problem of traffic congestion in a mixed flow of human-driven and autonomous vehicles. Communication technologies such as fifth-generation mobile communication systems, which are improving rapidly, enable vehicle-to-vehicle communication with a sufficiently small delay. We previously reported a strategy based on vehicle-to-vehicle communication for avoiding traffic congestion by using leader–follower control, which is a distributed autonomous control strategy. However, it was designed and evaluated for single-lane traffic
Kurishige, Masahiko
Precise vehicle state and the surrounding traffic information are essential for decision-making and dynamic control of intelligent connected vehicles. Tremendous research efforts have been devoted to developing state estimation techniques. This work investigates the research progress in this field over recent years. To be able to describe the state of multiple traffic elements uniformly, the concept of a vehicle neighborhood system is proposed to describe the system composed of vehicles and their surrounding traffic elements and to distinguish it from the traditional macroscopic traffic research field. In this work, the vehicle neighborhood system consists of three main traffic elements: the host vehicle, the preceding vehicle, and the road. Therefore, a review of state estimation methods for the vehicle neighborhood system is presented around the three traffic objects mentioned earlier. This article performs a comprehensive analysis of these approaches and depicts their strengths and
Wang, YanWei, HenglaiYang, LieHu, BinbinLv, Chen
Platooning vehicles present novel pathways to saving fuel during transportation. With the rise of autonomous solutions, platooning becomes an increasingly apparent sector requiring the application of this new technology. Platooning vehicles travel together intending to reduce aerodynamic resistance during operation. Drafting allows following vehicles to increase fuel economy and save money on refueling, whether that be at the pump or at a charging station. However, autonomous solutions are still in infancy, and controller evaluation is an exciting challenge proposed to researchers. This work brings forth a new application of an emissions quantification metric called vehicle-specific power (VSP). Rather than utilize its emissions investigative benefits, the present work applies VSP to heterogeneous Class 8 Heavy-Duty truck platoons as a means of evaluating the efficacy of Cooperative Adaptive Cruise Control (CACC). VSP creates a bridge between types of passenger vehicles to compare
Snitzer, PhilipStegner, EvanBentley, JohnBevly, David M.Hoffman, Mark
The advancement of Advanced Driver Assistance System (ADAS) technologies offers tremendous benefits. ADAS features such as emergency braking, blind-spot monitoring, lane departure warning, adaptive cruise control, etc., are promising to lower on-road accident rates and severity. With a common goal for the automotive industry to achieve higher levels of autonomy, maintaining ADAS sensor performance and reliability is the core to ensuring adequate ADAS functionality. Currently, the challenges faced by ADAS sensors include performance degradation in adverse weather conditions and a lack of controlled evaluation methods. Outdoor testing encounters repeatability issues, while indoor testing with a stationary vehicle lacks realistic conditions. This study proposes a hybrid method to combine the advantages of both outdoor and indoor testing approaches in a Drive-thru Climate Tunnel (DCT). The proposed DCT features a test section that is isolated from the surrounding environment and allows a
Pao, Wing YiLi, LongAgelin-Chaab, MartinKomar, John
The efficiency in energy consumption of an electric vehicle (EV) has significant value to both vehicle manufacturers and vehicle owners. Such efficiency will directly impact the cost of energy and vehicle range while relieving the stringent requirements on the DC motor and battery specs. Nowadays, with the development of advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), drivers enjoy a much safer driving experience. ADAS capabilities in sensory, computing and communication can be leveraged in EVs for the purpose of optimizing energy consumption. This paper introduces an energy-optimized ACC platform, which utilizes a forecast of the speed profile of the host vehicle in a short (few seconds) horizon. Such speed information can be available through ADAS or similar systems. This paper focuses on optimization in longitudinal tracks. We consider ten different drive-cycles in several driving scenarios, such as
Shahram, ShahriarPourmohammadi Fallah, Yaser
For cooperative adaptive cruise control (CACC) system, a robust following control algorithm based on fuzzy PID principle is adopted in this paper. Firstly, a nonlinear vehicle dynamics model considering the lag of driving force and acceleration constraints was established. Then, with the vehicle’s control hierarchic, the upper controller takes the relative speed between vehicles and the spacing error as inputs to output the following vehicle's target acceleration, while the lower controller takes the target acceleration as inputs and the throttle opening and brake master cylinder pressure as outputs. For the setting of target spacing, this paper additionally considers the relative speed between vehicles and the acceleration of the front vehicle. Through testing, compared with the traditional variable safety distance model, the average distance reduces by 5.43% when leading vehicle is accelerating, while increases by 2.74% in deceleration. For the fixed-speed cruise mode, a set of logic
Zhu, MingyangTan, Gangfeng
Platooning is a promising technology which can mitigate greenhouse gas impacts and reduce transportation energy consumption. Platooning is a coordinated driving strategy where trucks align themselves in order to realize aerodynamic benefits to reduce required motive force. The aerodynamic benefit is seen as either a “pull” effect experienced by the following vehicles or a “push” effect experienced by the leader. The energy savings magnitude increases nonlinearly as headway (following distance) is reduced [1]. In efforts to maximize energy savings, cooperative adaptive cruise control (CACC) is utilized to maintain relatively short headways. However, when platooning is attempted in the real world, small transient accelerations caused by imperfect control result in observed energy savings being less than expected values. This study analyzes the performance of a recently developed nonlinear model predictive control (NMPC) platooning strategy over challenging terrain. The NMPC strategy is
Bentley, John WilliamSnitzer, PhilipStegner, EvanBevly, David M.Hoffman, Mark
Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost. With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication. Upcoming road grade, traffic lights and lead vehicle speeds can be utilized to
Paul, SumitGoyal, VasuJoshi, SatyumFranke, MichaelTomazic, DeanZeman, Jonathan
Modern heavy vehicles may be equipped with an Advanced Driver Assistance System (ADAS) designed to increase highway safety. Depending on the vehicle or manufacturer, these systems may detect objects in a driver’s blind spot, provide an alert when the ADAS determines that the vehicle is leaving its lane of travel without the use of a turn signal, or notify the driver when certain road signs are detected. ADASs also include adaptive cruise control, which adjusts the vehicle’s set cruise speed to maintain a safe following distance when a slower vehicle is detected ahead of the truck. In addition, the ADAS may have a Collision Mitigation System (CMS) component that is designed to help drivers respond to roadway situations and reduce the severity of crashes. CMSs typically use radar or a combination of radar and optical technologies to detect objects such as vehicles or pedestrians in the vehicle’s path. If the CMS determines that a collision event is likely, interventions such as audible
Austin, TimothyGrimes, WesleyCheek, TimothyPlant, DavidSteiner, JohnHiggins, BradleyLombardi, KristinaDiSogra, MatthewWilcoxson, Gregory
The presented study is dedicated to the technology supporting vehicle state estimation and motion control with a concept drone, which helps the vehicle in sensing the surroundings and driving conditions. This concept allows also extending the functionality of the sensors mounted on the vehicle by replacing or including additional parameter observation channels. The paper discusses the feasibility of such a drone-vehicle interaction as well as demonstrates several design configurations. In this regard, the paper presents a general description of the proposed drone system that assists the vehicle and describes an experiment in measuring the profile of the road with a range sensor. The results obtained in the experiment are described in terms of the accuracy to be achieved using the drone and are compared with other studies, which use the methods of estimation from the sensors mounted on the vehicle. The proposed measurement concept can be applied to a large number of vehicle systems such
Beliautsou, ViktarBeliautsou, AleksandraIvanov, Valentin
Aiming to improve the lateral instability of adaptive cruise control (ACC) systems, both the front and rear vehicles are considered the centers of two control strategies. A vehicle control system is designed to enable the vehicle to automatically find the best following distance based on the displacement and speeds of the front and rear vehicles, hence enhancing driver assistance, traffic efficiency, and road utilization ratio. A practical model predictive control is designed to improve performance, responsiveness, and minor discomfort. A quadratic programming (QP) solver is used to construct an error preview-based mathematical model for the vehicle control, which is then applied to improve the control performance of the system to achieve relative intervehicle distance control. The time sampling of the parameters and the prediction horizon are obtained by numerical simulation, verifying the effectiveness of the ACC system proposed
Umar Yakubu, Al-aminGuoqing, GengQingyuan, Shen
Multiple object detection and tracking are central aspects of modeling the environment of autonomous vehicles. Lidar is a necessary component in the autonomous driving system. Without Lidar sensors, we will most probably not see fully self-driving cars become a reality. Lidar sensing gives us high-resolution data by sending out thousands of laser signals. In advanced driver assistance systems or automated driving systems, 3-D point clouds from lidar scans are typically used to measure physical surfaces. Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. Lidar can provide a complete 360-degree view of a scene. This paper designs Lidar based multi-target detection and tracking system based on the traditional point cloud processing method including down-sampling, denoising, segmentation, and clustering objects. Based on the detections from Lidar, a multi-target tracking system is involved in this paper which can be used on
Wu, ZhihongZhu, YuanLu, KeLi, Fu-Xiang
The recent proliferation of perception sensing and computing technologies has promoted the rapid development of automated driving. The design of the perception sensing system has nonnegligible influences both on the performances of various automated driving features and on the system costs. This paper proposes an automated driving feature oriented framework for automatic selection and arrangement of the sensors in the perception sensing system. An automated driving feature oriented optimization model is built considering the characteristics and requirements of the specific feature and a genetic algorithm based design method is provided to solve this optimization model. Furthermore, the Adaptive Cruise Control feature and the Automated Parking Assistance feature are selected as the simulation cases to verify the effectiveness of the proposed method. The proposed method has prospective potential to provide an automatic generation framework for the sensor selection and arrangement scheme
Meng, TianchuangHuang , JinZhang, BoweiHao, JianpingJia, YifanYang, DiangeZhong, Zhihua
Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track. Finally, the tracking algorithm is tested on the vehicle equipped with millimeter radar and the result
Wu, ZhihongLi, Fu-XiangZhu, YuanLu, Ke
Simulation of real time situations is a time tested software validation methodology in the automotive industry and array of simulation technologies have been in use for decades and is widely accepted and been part & parcel of software development cycle. While software that is being developed needs detailed plan, architecture and detailed design, it also matters during its development that, it is built in the right way from the very beginning and is fine tuned constantly. Especially for Software-In-Loop simulation (SIL), plenty of practices/tools/techniques/data are being used for simulation of system/software behavior. When it comes to choosing the right simulation technique and tools to be adopted, often there are discussions revolve around cost, feasibility, effectiveness, man-power, scalability, reusability etc. As automotive software validation is data driven, we deal with myriad of ground truth data for simulations, ranging from vehicle dynamics to vehicle models to environment
Nagarajan, KalaiyarasanRanga, AnkurKalkura M, KiranAnegundi, RanishreeAriharan, Anantharaju
At present, the 77GHz millimeter-wave (MMW) radar is considered to be the most promising vehicle sensor in the automatic vehicle perception system. Although MMW radar is less affected by the weather and can reliably obtain information in bad weather, it does not mean that MMW radar is completely immune to weather. Aiming at the maximum detection range attenuation of the MMW radar in extreme weather, the article constructs the detection range attenuation model of the MMW radar in different weather conditions. Aiming at the impact of MMW detection attenuation on the environmental perception of autonomous driving, Autonomous Emergency Braking (AEB) and adaptive cruise control (ACC) algorithms are designed. We established the model and algorithm on the CARLA virtual simulation platform and simulated MMW radar detection attenuation to test the driving safety of automatic driving under different weather conditions. The simulation results show that MMW radar can well perceive the surrounding
Bi, XinWeng, CaienTong, PanpanLi, DehaiYang, XiongjiZhao, Guiquan
This work presents a multi-objective adaptive cruise control (ACC) system via deep reinforcement learning (DRL). During the control period, it quantitatively considers three indexes: tracking accuracy, riding comfort, and fuel economy. The system balances contradictions between different indexes to achieve the best overall control results. First, a hierarchical control architecture is utilized, where the upper level controller is synthesized under DRL framework to give out the vehicle desired acceleration. The lower level controller executes the command and compensates vehicle dynamics. Then, four state variables that can comprehensively determine the car-following states are selected for better convergence. Multi-objective reward function is quantitatively designed referring to the evaluation indexes, in which safety constraints are considered by adding violation penalty. Thereafter, the training environment which excludes the disturbance of preceding car acceleration is built. And
Zhang, YourongLin, LiSong, YizhouHuang, Kaisheng
In advanced driver assistance systems (ADAS) or autonomous driving Systems (ADS) the robust and reliable perception of the environment, especially for the detecting and tracking the surrounding vehicle is prerequisite for collision warning and collision avoidance. In this paper a post-fusion tracking approach is presented which combines the front view Radar observation and front smart camera information. The approach can improve the tracking accuracy of the tracking system to support ADAS or ADS function such as adaptive cruise control (ACC) or autonomous emergency braking (AEB). The paper describes the state estimation algorithm, data association in the fusion architecture. Furthermore, the fusion architecture is tested and validated in real highway driving scenario
Li, Fu-XiangWu, ZhihongZhu, YuanLu, Ke
Vehicle speed controls, as adaptive cruise control and related automated evolutions, are control systems able to follow a desired vehicle reference speed that is set by the driver and fused with information as road signs, SD maps etc.. Current normal production systems don’t distinguish among the vehicle users, only some carmakers are doing first steps towards the introduction of learning from driver to adapt the traditional control. In our work, we follow up this content with a humanized speed control, based on learning of driver longitudinal behavior. This method is able to combine machine learning algorithms, vehicle positioning and recurrent trips into existing automated longitudinal control systems. Proposed algorithm can reduce the interactions between drivers and automated systems by improving the acceptance of automated longitudinal control. Furthermore, proposed integration works mainly on speed reference that dramatically simplifies the customization of the system. We present
Raffone, EnricoFossanetti, MassimoRei cEng, Claudio
Platooning heavy-duty trucks decreases aerodynamic drag for following trucks, reducing energy consumption, and increasing both range and mileage. Previous platooning experimentation has demonstrated fuel economy benefits in two-, three-, and four-truck configurations. However, exogenous variables disturb the ability of these platoons to maintain the desired formation, causing an accordion effect within the platoon and reducing energy benefits via acceleration/deceleration events. This phenomenon is increasingly exacerbated as platoon size and road grade variations increase. The current work assesses how platoon size, road curvature, and road grade influence platoon energy efficiency. Fuel consumption rate is experimentally quantified for four heterogeneous Class 8 vehicles operating in standalone (baseline), two-, and four-truck platooning configurations to assess fuel consumption changes while driving through diverse road conditions. Platooning was accomplished via PID-based
Snitzer, PhilipStegner, EvanSiefert, JanBevly, David M.Hoffman, Mark
The advances in automotive technology continue to deliver safety and driving comfort benefits to society. The Automated Driving Assistance System (ADAS) technology is at the forefront of this evolution. Today, various vehicle models on the road have features like lane centering, automated emergency braking, adaptive cruise control, traffic jam assist etc. During early development, such feature algorithms often assume ideal environmental and vehicle conditions while doing performance evaluation. It is imperative that one uses realistic scenarios for production development. To demonstrate this, the lane centering ADAS feature performance is studied using a test vehicle. The feature considered here is an end-to-end feature, i.e., from camera sensor output to steering actuation. Lane centering control system often has multiple control loops within the vehicle system. The delay in steering system response has a significant effect on overall lane centering performance and driver feel. This
Awathe, ArpitVarunjikar, TejasGanguli, Subhabrata
Adaptive cruise control (ACC) is an enhancement of conventional cruise control systems that allows the ACC-equipped vehicle to follow a forward vehicle at a pre-selected time gap, up to a driver selected speed, by controlling the engine, power train, and/or service brakes. This SAE Standard focuses on specifying the minimum requirements for ACC system operating characteristics and elements of the user interface. This document applies to original equipment and aftermarket ACC systems for passenger vehicles (including motorcycles). This document does not apply to heavy vehicles (GVWR > 10,000 lbs. or 4,536 kg). Furthermore, this document does not address other variations on ACC, such as “stop & go” ACC, that can bring the equipped vehicle to a stop and reaccelerate. Future revisions of this document should consider enhanced versions of ACC, as well as the integration of ACC with Forward Vehicle Collision Warning Systems (FVCWS
Advanced Driver Assistance Systems (ADAS) Committee
Modern safety and comfort features must behave country specific to the local environment and traffic conditions in order to gain end consumers’ trust and strengthening OEMs market success respectively. In order to achieve this, a new methodology was developed. In this paper, the approach for designing advanced driving assistance systems (ADAS) with a tailored controller behavior optimized for country specific market expectations like in India is described. Furthermore, the definition of objective performance and calibration targets with automated evaluation of target fulfillment will be deeply discussed. The method is focused on saving time at calibration and validation without compromising the quality of ADAS features. Local market specific driving behavior is investigated and measurement data from real-world driving collected. Data clustering via maneuver detection is performed automatically, which is saving time and effort. The target values for the performance KPIs are extracted
Quinz, PhilippScheidel, StefanHasenbichler, GernotRamschak, Erich
Autonomous vehicle is a vehicle capable of sensing its environment and taking decisions automatically with no human interventions. To achieve this goal, ADAS (Advance Driving Assistance System) technologies play an important role and the technologies are improving and emerging. The sensing of environment can be achieved with the help of sensors like Radar and Camera. Radar sensors are used in detecting the range, speed and directions of multiple targets using complex signal processing algorithms. Radar with long range and short range are widely used in the autonomous vehicles. Radar sensors with long range can be used to realize features like Adaptive Cruise Control, Advance Emergency Brake Assist. The short-range radar sensors are used for Blind Spot Monitoring, Lane Change Assist, Rear/Front Cross Traffic Alert and Occupant Safe Exit. To realize the Autonomous vehicle functionalities four short range radar sensors are required, two on front and two on rear (left and right). This
Sujeendra, M RKesana, Sindhu PrabhaSaddaladinne, Jagadeesh Babu
This document describes [motor] vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for six levels of driving automation, ranging from no driving automation (Level 0) to full driving automation (Level 5), in the context of [motor] vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways: Level 0: No Driving Automation Level 1: Driver Assistance Level 2: Partial Driving Automation Level 3: Conditional Driving Automation Level 4: High Driving Automation Level 5: Full Driving Automation These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on [motor] vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit
On-Road Automated Driving (ORAD) Committee
The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world
Borhan, HoseinaliLammert, MichaelKelly, KennethZhang, ChenBrady, NathanYU, Chia-SiungLiu, Jingxuan
Fuel savings from truck platooning are generally attributed to an aerodynamic drag-reduction phenomena associated with close-proximity driving. The current paper is the third in a series of papers documenting track testing of a two-truck platoon with a Cooperative Adaptive Cruise Control (CACC) system where fuel savings and aerodynamics measurements were performed simultaneously. Constant-speed road-load measurements from instrumented driveshafts and on-board wind anemometry were combined with vehicle measurements to calculate the aerodynamic drag-area of the vehicles. The drag-area results are presented for each vehicle in the two-truck platoon, and the corresponding drag-area reductions are shown for a variety of conditions: gap separation distances (9 m to 87 m), lateral offsets (up to 1.3 m), dry-van and flatbed trailers, and in the presence of surrounding traffic. For the standard aligned platoon, the results demonstrate up to 8% drag reduction for the lead vehicle, with drag
McAuliffe, BrianSmith, PatrickRaeesi, ArashHoffman, MarkBevly, David M.
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