Browse Topic: Adaptive cruise control
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
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
Letter from the Focus Issue Editors
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
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
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
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
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
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
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
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
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
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