Browse Topic: Cruise control
ABSTRACT Autonomous robots can maneuver into dangerous situations without endangering Soldiers. The Soldier tasked with the supervision of a route clearing robot vehicle must be located beyond the physical effect of an exploding IED but close enough to understand the environment in which the robot is operating. Additionally, mission duration requirements discourage the use of low level, fatigue inducing, teleoperation. Techniques are needed to reduce the Soldier’s mental stress in this demanding situation, as well as to blend the high level reasoning of a remote human supervisor with the local autonomous capability of a robot to provide effective, long term mission performance. GDRS has developed an advanced supervised autonomy version of its Robotics Kit (GDRK) under the Robotic Mounted Detection System (RMDS) program that provides a cost effective, high-utility automation solution that overcomes the limitations and burden of a purely teleoperated system. GDRK is a modular robotic
In recent decades, significant technological advances have made cruise control systems safer, more automated, and available in more driving scenarios. However, comparatively little progress has been made in optimizing vehicle efficiency while in cruise control. In this paper, two distinct strategies are proposed to deliver efficiency benefits in cruise control by leveraging flexibility around the driver’s requested set speed, and road information that is available on-board in many new vehicles. In today’s cruise control systems, substantial energy is wasted by rigidly controlling to a single set speed regardless of the terrain or road conditions. Introducing even a small allowable “error band” around the set speed can allow the propulsion system to operate in a pseudo-steady state manner across most terrain. As long as the vehicle can remain in the allowed speed window, it can maintain a roughly constant load, traveling slower up hills and faster down hills. This strategy reduces the
This SAE Information Report provides a compendium of terms, definitions, abbreviations, and acronyms to enable common terminology for use in engineering reports, diagnostic tools, and publications related to active safety systems. This information report is a survey of active safety systems and related terms. The definitions offered are descriptions of functionality rather than technical specifications. Included are warning and momentary intervention systems, which do not automate any part of the dynamic driving task (DDT) on a sustained basis (SAE Level 0 as defined in SAE J3016), as well as definitions of select features that perform part of the DDT on a sustained basis (SAE Level 1 and 2
This document provides a mapping between provider service identifiers (PSIDs)—allocated to SAE by the appropriate registration authorities—and SAE technical specifications of applications identified by those PSIDs. It is intended that this document will be updated regularly, including information about the publication status of SAE technical reports
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
SAE J2461 specifies the recommended practices of a Vehicle Electronics Programming Stations (VEPS) architecture.in a Win32® environment. This system specification, SAE J2461, was a revision of the requirements for Vehicle Electronics Programming Stations (VEPS) set forth in SAE J2214, Vehicle Electronics Programming Stations (VEPS) System Specification for Programming Components at OEM Assembly Plants (Cancelled Jun 2004). The J2214 standard has been cancelled indicating that it is no longer needed or relevant
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
Automobile sector is growing every day with fast affinity towards Autonomous vehicles. The most challenging task of ADAS based driverless car is to identify and track the objects in front of the vehicle. To implement this type of technology we require a robust algorithm which can classify the object just-in-time and have great accuracy. We are using automotive radar sensor of 77GHz frequency. Quite often we’ve noticed sudden fluctuations in prediction of the obstacles using either heuristic or even machine learning techniques which focus only on frame-wise / cycle-wise data. So, this inspires us to investigate the history of the data coming in as opposed to only one cycle at a time. Hence, we incorporated a technique wherein we could make use of the past data as well as current cycle data. In this paper, we’ve used Radar time series data to classify the object in front of the Ego vehicle in each Radar cycle. The time series data collected from RADAR enables the reliable prediction of
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
The terms and definitions in this document describe the functions performed within an ADS, as defined in SAE J3016. Where possible we have attempted to capture the language that is already in use within the automated driving development community. Where needed, we have added new terms and definitions, including clarifying notes to avoid ambiguity. SAE J3131 deals primarily with Level 4 and Level 5 ADS features
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
In previous work, AC Compressor Cycling (ACC) was modeled by incorporating evaporator thermal inertia in Mobile Air Conditioning (MAC) performance simulation. Prediction accuracy of >95% in average cabin air temperature has been achieved at moderate ambient condition, however the number of ACC events in 1D CAE simulation were higher as compared to physical test [1]. This paper documents the systematic approach followed to address the challenges in simulation model in order to bridge the gap between physical and digital. In physical phenomenon, during cabin cooldown, after meeting the set/ target cooling of a cabin, the ACC takes place. During ACC, gradual heat transfer takes place between cold evaporator surface and air flowing over it because of evaporator thermal inertia. In earlier work, the ‘evaporator exit air temperature’ has been used to model ACC, whereas in the current work, the ‘evaporator exit air temperature’ is replaced by ‘point mass exit air temperature’ to simulate
The U.S. Environmental Protection Agency (EPA) certifies gasoline deposit control additives for intake valve deposit (IVD) control utilizing ASTM D5500, a vehicle test using a1985 BMW 318i. Concerns with the age of the test fleet, its relevance in the market today, and the availability of replacement parts led the American Chemistry Council’s (ACC) Fuel Additive Task Group (FATG) to begin a program to develop a replacement. General Motors suggested using a 2.4L LE9 test engine mounted on a dynamometer and committed to support the engine until 2030. Southwest Research Institute (SwRI®) was contracted to run the development program in four Phases. In Phase I, the engine test stand was configured, and a test fuel selected. In Phase II, a series of tests were run to identify a cycle that would build an acceptable level of deposits on un-additized fuel. In Phase III, the resultant test cycle was examined for repeatability. In Phases IVa and IVb, two discrimination matrices evaluated the
This SAE Standard defines the test conditions, procedures, and performance specifications for 6 V and 12 V stop lamp switches intended for use on motorcycles
Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each other in close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces greenhouse gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel. This is especially an issue if deceleration is applied in response to a bad
Presently, a main mobility sector objective is to reduce its impact on the global greenhouse gas emissions. While there are many techniques being explored, a promising approach to improve fuel economy is to reduce the required energy by using slipstream effects. This study analyzes the demanded engine power and mechanical energy used by heavy-duty trucks during platooning and non-platooning operation to determine the aerodynamic benefits of the slipstream. A series of platooning tests utilizing class 8 semi-trucks platooning via Cooperative Adaptive Cruise Control (CACC) are performed. Comparing the demanded engine power and mechanical energy used reveals the benefits of platooning on the aerodynamic drag while disregarding any potential negative side effects on the engine. However, energy savings were lower than expected in some cases. It was hypothesized that the CACC may have amplified transient platooning events relative to the individual truck baseline results, hampering the
Autonomy for multiple trucks to drive in a fixed-headway platoon formation is achieved by adding precision GPS and V2V communications to a conventional adaptive cruise control (ACC) system. The performance of the Cooperative ACC (CACC) system depends heavily on the reliability of the underlying V2V communications network. Using data recorded on precision-instrumented trucks at both ACM and NCAT test tracks, we provide an understanding of various effects on V2V network performance: Occlusions - non-line-of-sight (NLOS) between the Tx and Rx antenna may cause network signal loss. Rain - water droplets in the air may cause network signal degradation. Antenna position - antennas at higher elevation may have less ground clutter to deal with. RF interference - interference may cause network packet loss. GPS outage - outages caused by tree cover, tunnels, etc. may result in degraded performance. Road curvature - curves may affect antenna diversity. Road grade - antenna may have limited
There are a large number of curves and slopes in the mountainous areas. Unreasonable acceleration and deceleration in these areas will increase the burden of the brake system and the fuel consumption of the vehicle. The main purpose of this paper is to introduce a speed planning and promotion system for commercial vehicles in mountainous areas. The wind, slope, curve, engine brake, and rolling resistances are analyzed to establish the thermal model of the brake system. Based on the thermal model, the safe speed of the brake system is acquired. The maximum safe speed on the turning section is generated by the vehicle dynamic model. And the economic speed is calculated according to the fuel consumption model. The planning speed is provided based on these models. This system can guide the driver to handle the vehicle speed more reasonably. According to the simulation, compared to cruise control, speed planning can save fuel consumption at a mean value of 9.13% in typical mountainous areas
A new Cruise Control Algorithm (CCA) commanding the Internal Combustion Engine (ICE) and the Continuous Variable Transmission (CVT) of a 200 hp tractor was implemented on a Rapid Prototyping System (RPS) and successfully tested with an empty vehicle and with 16 t trailer from 0.5 to 50 kph. Low velocities required an extra controller and a good concept for transition to higher velocities
Adaptive Cruise Control (ACC) includes three modes: cruise control, car following control, and autonomous emergency braking. Among them, the car following control mode is mainly used to manage the speed and vehicle spacing approach the preceding vehicle within the range of smooth acceleration changes. In addition, although the motion information signal of the preceding vehicle can be collected by auxiliary equipment, it is still a random variable and normally regarded as a disturbance to affect the performance of vehicle controller. Therefore, this paper proposed an ACC strategy considering the disturbance of the preceding vehicle and multi-objective optimization. First, the switching strategy was designed according to the relationship between the collision time, time headway, and working characteristics of brake system; Then, we built a vehicle-following model considering the disturbance of the preceding vehicle, and designed a Model Predictive Controller to smoothly control the
Items per page:
50
1 – 50 of 484