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The CAN system
Published in Allan Bonnick, Automotive Powertrain Science and Technology, 2020
Adaptive cruise control uses radar and, in some cases, a camera to ascertain the distance between a moving vehicle and a vehicle in front (moving in the same direction). After the driver selects the desired cruising speed, the radar signals, as recorded by the ECU, are used to determine what action if any is required to maintain speed and a safe distance between vehicles. If acceleration is required, the cruise control ECU connects with the engine ECU (via CAN) to increase fuelling in a diesel engine, or open the throttle in a petrol engine. If the speed of the vehicle exceeds the set speed or the vehicle is getting too close to the one in front the system will, through the engine ECU, intervene to slow the vehicle down. If the situation is severe, such as the vehicle in front making an emergency stop, the brakes may be applied through the brake system ECU. This action is facilitated because wheel brakes can be operated via CAN without driver action. A later development that is used in some trucks makes use of GPS. The cruise control settings in the map in the control unit are designed to take advantage of GPS information so that the system can take account of the terrain in different situations. In the event of an emergency such as a vehicle cutting in front of another, the required level of braking may be higher than that provided by the adaptive system, in which case the driver is alerted, by audible signal, to apply the brakes accordingly.
Case studies
Published in Tom Denton, Automated Driving and Driver Assistance Systems, 2019
Today, adaptive cruise control already tracks the vehicles ahead and adapts the distance and speed of the driver’s own vehicle accordingly. Acting in combination with the ESP® system and with the additional support of lane-detection cameras and electromechanical steering, this forms the technical basis for autonomous driving. High-performance software now calculates the appropriate driving instructions for a safer and less stressful driving. Automatic lane changing is the next functional step. It calls for two additional features. First, a rear-mounted radar sensor that also detects fast-approaching vehicles and, second, a dynamic navigation map. Such maps, which operate via a mobile network connection, can keep drivers informed of current roadwork sites and local speed restrictions. And although drivers remain responsible for driving, they can limit themselves to monitoring the actions of the driver assistance system.
New Technologies, Vehicle Features, and Technology Development Plan
Published in Vivek D. Bhise, Automotive Product Development, 2017
3.Adaptive Cruise Control System: Adaptive cruise control (also called autonomous or radar cruise control) is an optional cruise control system that automatically adjusts the vehicle speed to maintain a safe distance from vehicles ahead. The control is based on sensor information from onboard sensors (radar or laser based). Most systems provide steering wheel–mounted controls for setting maximum cruising speed and safe headway distance from the leading vehicle.
Impact of adaptive cruise control (ACC) system on fatality and injury reduction in China
Published in Traffic Injury Prevention, 2021
Hong Tan, Fuquan Zhao, Zongwei Liu
The adaptive cruise control (ACC) system is considered an effective active safety system for avoiding rear-end and sideswipe collisions. ACC is an advanced assistance system that adjusts vehicle velocity and provides a specified distance to the preceding vehicle by automatically controlling the throttle and/or the brake based on the environmental data available from vehicle sensors (such as radar, lidar, or a camera; Varotto et al. 2020). An ACC-equipped vehicle travels at a user-set velocity when there is no preceding vehicle. An independent evaluation reported that ACC systems would prevent 6% to 15% of all rear-end crashes in the United States each year (Najm et al. 2006). In another project (Deployment of Interurban ATT Test Scenarios (DIATS) 1998), it was reported that the average safety improvement for lane-change collisions and rear-end collisions was 8% and 20%, respectively. In another study, a 45% reduction in fatalities and a 30% reduction in injuries in rear-end collisions was estimated because the frequency of dangerous headways decreases when the ACC function is turned on (Wilmink et al. 2008). In addition, drivers with ACC were inclined to perform 36% fewer lane changes in traffic in a naturalistic driving study (Schakel et al. 2017). Rather than overtaking, participants chose to stay in the slower lane and let ACC follow the predecessor. Additional references can be found in Appendix B (see online supplement).
New England merge: a novel cooperative merge control method for improving highway work zone mobility and safety
Published in Journal of Intelligent Transportation Systems, 2021
Tianzhu Ren, Yuanchang Xie, Liming Jiang
Similar to late merge and early merge, implementing this NEM in practice would benefit from careful planning and proper law enforcements, such as setting up dynamic message signs and automated photo enforcement. With the developments in connected and automated vehicle technologies, the NEM will become increasingly applicable. In fact, some vehicles today are already equipped with the adaptive cruise control with stop-and-go technology, which automates vehicle longitudinal control and allows drivers to specify the desired speed and distance headway. Such a technology is ideally suited for NEM control. Technically, it is not difficult to add vehicle-to-infrastructure (V2I) capability to these vehicles so that they can receive NEM control instructions from roadside units associate with work zones.
Human–Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal
Published in International Journal of Human–Computer Interaction, 2019
Francesco Biondi, Ignacio Alvarez, Kyeong-Ah Jeong
These four stages can be automated, on a control continuum from low automation to high automation (as in Sheridan & Verplank, 1978). Information acquisition applies to sensing of information and registration of environmental inputs. At a low level of automation, this may entail having sensors that scan the road environment. A highly automated sensing system, instead, may classify road objects depending on their characteristics and dimensions (pedestrians, bicyclists, passenger vehicles, trucks, etc.). System information analysis refers to cognitive functions such as working memory and inferential processing (Parasuraman et al., 2000). While a low-automated system may provide information about the trajectory of road objects, a system with a higher level of automation in this domain may inform the human operator about the potential for a collision between the vehicle and other road agents. Decision selection is the ability to make decisions and select actions. Within the realm of vehicle automation, this describes systems recommending the human driver to execute certain safety-relevant maneuvers (“BRAKE” when the following distance is shorter than a safety threshold). Action implementation refers to the ability to execute specific actions that may or may not have been selected with the assistance of the automated systems. Cruise Control represents an example of a system with low automation, while Adaptive Cruise Control, with its ability to automate both distance keeping and speed maintenance, is a system with a higher level of automation.