Adaptive Cruise Control

Connected and autonomous vehicle (CAV) technologies are likely to be gradually implemented over time. In this paper, an adaptive cruise control, named Smart Driver Model (SDM), is proposed to describe the autonomous vehicles flow. The stability criteria is proposed for SDM to judge the stability of homogeneous traffic flow. Numerical simulations were conducted to verify the results of the theoretical analysis. Single-lane vehicle dynamics in a traffic stream with connected and autonomous vehicles are simulated by varying model parameters. Simulation results are consistent with the results of linear stability analysis. As a result, a set of parameters is proposed to investigate the stabilization effect of the proposed model on homogeneous traffic flow considering realistic driving cycle and cut-in condition. By simulating a platoon with a lead vehicle which follows the Urban Dynamometer Driving Schedule (UDDS), we find out that the proposed model can stabilize the traffic flow with proposed parameters. The results from simulation and linear stability analysis show that SDM outperforms the IDM-ACC and the ACC proposed by Milanés and Shladover in terms of stabilization effect on homogeneous traffic flow. The simulation result shows that the SDM- equipped vehicles are able to stabilize the homogeneous traffic flow under cut-in condition.

Themann et al. (2015) proposed a control model for Adaptive Cruise Control systems (ACC) that relied on the optimisation of the velocity profile with respect to fuel consumption. This study used Dijkstra’s algorithm to find the optimal velocity profile for known road topography. Porsche’s Innodrive ACC has also adopted a similar approach, resulting in about 10% reduction in fuel consumption (Markschläger, et al., 2012). Hellström et al. (2010) developed a fuel-optimal control model for trucks. In this study, prior knowledge of road topography was used in order to optimise fuel consumption and gear-shifting, and the problem was formulated as a dynamic programming optimisation. In all these studies, fuel economy is obtained by producing a smooth velocity performance and avoiding unnecessary accelerations. Kohut et al. (2009) achieve the same objective by adopting a Model Predictive Control (MPC) framework. This study highlights the trade-off between fuel savings and trip time.

This paper gives the thought to create Adaptive cruise control by utilizing sensors and two Raspberry Pi boards. The proposed framework comprises two Raspberry pi, one acts as slave module and another one acts as master. This slave module can be used to recognition different parameters like Distance between vehicle by utilizing Ultrasonic sensor, Wet weather monitoring by utilizing Humidity sensor, Road slope detection by utilizing Accelerometer, Damaged road detection by utilizing Vibration sensor, Temperature monitoring by utilizing Temperature sensor, Location finding by utilizing GPS, Speed detection by utilizing RPM meter and master is responsible for controlling dc motor. Here both Raspberry pi, slave is associated with master through TCP/IP protocol. This TCP/IP protocol is used for exchange the information between salve module and master module. Salve sensor values must be defined as threshold values should be less, if those measured value are more than threshold values then master will produces PWM signals. By using PWM signals master will controlling speed of the dc motor and lastly values are displayed on LCD.

The recent advances in adaptive control and autonomous vehicles have given rise to the studies on cooperative control of road vehicles, and the consequent effects on traffic flow performances. In order to find an optimum penetration rate of vehicles with Cooperative Adaptive Cruise Control (CACC) for varying scenarios of mobility demand and road geometry, we have explicitly considered two measures as the total time spent and the total emissions are documented in the literature to be correlated with each other [1]. We have selected the total time spent and total emissions on purpose to have an insight about the effectiveness of the CACC aiming to obtain a steady state of traffic flow. We have tested two hypothetical road networks with different levels of complexity, in which all the nodes are assumed to behave as a signalized intersection. For simulation-based analyses, we have made use of the open source microscopic simulation software Eclipse Simulation of Urban MObility (SUMO) [2]. We have assumed in our analyses that at the intersections: in a range of 100 meters all the vehicles are in Vehicle to Vehicle (V2V) communication, as in [3]; and the minimum headway between cooperatively controlled vehicles equals 2 seconds. For the implementation of our assumptions, CACC vehicles are integrated in Eclipse SUMO via Omnet++ and Veins, and the needed arrangements are designed in Eclipse

In recent years by evolution of advanced driver assistant systems (ADAS), the intervention of driver in controlling the vehicle has been decreased extremely. One of the major reasons for this evolution is large number of accidents that cause injuries and deaths. It is estimated that 90% of accidents occur due to human error, potentially induced by distraction, poor judgment or lack of situation awareness [1]. Adaptive cruise control (ACC) is one of the best ADAS systems that can prevent the accident by controlling the distance between the vehicles. This system first used in luxury vehicles as an optional system to influence traffic

The Intelligent Vehicle Initiative (IVI) provides vehicle-based tools that could assist drivers in reacting both more rapidly and effectively to a range of external stimuli. Intelligent or adaptive cruise control systems (ICC or ACC), for example, attempt to assist drivers in better maintaining a safe headway under normal driving conditions. In addition, automatic braking systems may provide additional safety benefits by assisting drivers to respond more quickly to unexpected events.

The cars of tomorrow [8] will be more and more equipped with Advanced Driver Assistance Systems (ADAS) to support the driver in the driving task. One of the ADAS is the Adaptive Cruise Control (ACC). Pauwelussen and Minderhoud in [8] noted that the ACC could be defined as an extension of the Cruise Control (CC) and maintains, next to a certain set speed, a certain set distance with respect to the lead vehicle.

With a constant demand for traffic information on the surrounding traffic conditions and with the recent developments of vehicle-to-vehicle communication via Vehicular Ad-hoc Networks (VANETs), a recent potential of vehicles sharing traffic information to tackle traffic congestion and impact the traffic dynamics positively started to appear. Wireless communication via VANETs has been adopted due to the great advantages offered by the technology allowing high mobility, efficiency, and also being economically feasible. Cooperative Adaptive Cruise Control (CACC) is a more advanced technology providing the equipped vehicles with more accurate information about the preceding vehicle through speedy and real-time vehicle-to- vehicle traffic data sharing among CACC equipped vehicles. The effect of CACC on the traffic flow and safety is still vague due to the deficiency of research in this area.

Task order 6202, or “Effects of cooperative adaptive cruise control on traffic flow: testing drivers’ choices of following distances” is a PATH research on cooperative adaptive cruise control (CACC). The goal of the research is to acquire knowledge about drivers' behaviour regarding the usage of (the gap settings in) CACC and ACC systems and about the differences in behaviour between those two systems. Two test vehicles collect the data, one equipped with ACC and the other with CACC. In addition, several test subjects were driving these vehicles, up to 11 on the time of writing [15 July 2009]. Each drove for two weeks, consisting of 2 baseline days, 2 CACC days and 9 ACC days. While driving CACC, the participant was following the ACC vehicle driven by a PATH employee. Appendix 10.1 contains the protocol that the drivers were required to follow, along with a timetable of the different driving days. The working of the ACC and CACC systems is dependent on the speed of the vehicle. The table below gives the working of both ACC and CACC according to different vehicle speeds.

attempts other than this paper have been found at creating an ACC system with following control that incorporates consideration of the vehicle sideslip. Our research team has already submitted a patent application for this concept [13]. This paper describes the Adaptive Cruise Control system (ACC), a system which reduces the driving burden on the driver. The ACC system primarily supports the four driving modes on the road that are described in Section 2.1, and controls the acceleration and deceleration of the vehicle in order to maintain a set speed or to avoid a collision. The key to achieving intelligent ACC control is the method used to detect and follow the preceding vehicle.

The concept of assisting driver in the task of longitudinal vehicle control is known as cruise control. Starting from the cruise control devices of the seventies and eighties, now the technology has reached cooperative adaptive cruise control. This paper will address the basic concept of adaptive cruise control and the requirement to realize its improved versions including stop and go adaptive cruise control and cooperative adaptive cruise control. The conventional cruise control was capable only to maintain a set speed by accelerating or decelerating the vehicle. Adaptive cruise control devices are capable of assisting the driver to keep a safe distance from the preceding vehicle by controlling the engine throttle and brake according to the sensor data about the vehicle. Most of the systems use RADAR as the sensor .a few use LIDAR also. Controller includes the digital signal processing modules and microcontroller chips specially designed for actuating throttle and brake. The stop and go cruise control is for the slow and congested traffic of the cities where the traffic may be frequently stopped. Cooperative controllers are not yet released but postulations are already there. This paper includes a brief theory of pulse Doppler radar and FM-CW LIDAR used as sensors and the basic concept of the controller. [2]

Research is booming in the field of vehicle automation with prime motivation in improving driving comfort. The road traffic issues are booming to a new height as the number of vehicles is increasing day by day. Driving through an urban area is tiresome. If a control system can take you through this traffic congestion it will be a boon to car drivers whose life is depending on mobility. Cruise Control (CC) [1] was the first driver assistant launched into the market which can monitor the speed of the car. Later on CC was improved to Adaptive Cruise Control (ACC) [1] with additional radars [11] and cameras. The entire control system has been remodelled in such a way that even the system takes the control of the car in locations where it is stressful to manoeuvre. Things moved into a new breed of control system on advancing ACC to Cooperative Adaptive Cruise Control (CACC) [2] [3]. The concept of platoon with inter-vehicle communication system and utilizing GPS [15] for precise manoeuvring along with String Stability has improved the vehicle density with drastic reduction in road accidents. The report gives an insight to CACC through ACC.

Keeping the right distance to its predecessor is the main objective of the CACC controller and it is often referred to as individual vehicle stability. Besides this one, there is a second control objective: maintaining string stability. For a vehicle string to be string stable, there must be a guarantee that fluctuations in the speed of a car are attenuated upstream. This means that following cars should have fewer fluctuations than the preceding car in terms of the signal norm of for example speed [5] [6]. The string stability of a vehicle platoon is very important to prevent the generation of phantom traffic jams. These jams occur when a car breaks too strong, which causes its follower to break even harder to prevent a collision. This behaviour propagates backwards through the traffic, where finally cars will stand still. If a vehicle platoon is string stable, the inter-vehicle time headway is chosen in such a way, that a following car may always break less hard than its predecessor without colliding.

and the investigation of new solutions and technologies. As a result, this field has emerged as a crucial entity in the evaluation of traffic policies and technological developments within the transport sector. Automated driving is one of the areas where micro-simulation has a lot to offer. Micro-simulation with its potential to capture and analyse subtle traffic-related phenomena can play an important role in the investigation of different issues arising from ACCs and the validation of different proposals relating to their application. Moreover, acceleration models and lane- changing models that are fundamental aspects of micro-simulation can be considered as a foundation on which ACCs and autonomous vehicles can be built. For instance, the question of “what are the required explanatory variables in order to plausibly model driving behaviour?” that is highly relevant to micro-simulation, can also be directly linked to the sensory requirements of ACCs. The control and decision making processes [37] are some of the other subjects widely investigated within modelling driving behaviour that can lay down the basis for the development of automated driving. In order to derive a microscopic model of driving behaviour one needs to address the question of the variables that motivate drivers’ actions. However, the trade-off between simplicity and accuracy plays an important role in this issue. For instance, consider the process of lane. In a congested road where sufficient gaps in order to allow a lane-changing manoeuvre to be performed may not always be available, and in a scenario where a car from the most right lane needs to go to the most left lane in order to enter an off-ramp, forced merging and courtesy yielding plays a critical role. One can even go as far as to discuss the impacts of drivers’ attitudes and personalities in such a process. While such complex models could be potentially developed,

of the other nodes. Multicasting and Broadcasting is supported by CAN. To improve the behavior of the vehicle even further, it was necessary for the different control systems (and their sensors) to exchange information. This was usually done by discrete interconnection of the different systems (i.e. point to point wiring). The requirement for information exchange has then grown to such an extent that a cable network with a length of up to several miles and many connectors was required. This produced growing problems concerning material cost, production time and reliability.

In May 1998, Toyota became the first to introduce an ACC system on a production vehicle when it unveiled a laser- based system for its progress compact luxury sedan, which it sold in Japan. Then Nissan followed suit with a radar- based system, in the company’s Cima 41LV-2, a luxury sedan also sold only in Japan. In September 1999, Jaguar began offering an ACC for its XKR coupes and convertibles sold in Germany and Britain. Delphi Delco Electronic Systems supplies the radar sensing unit; TRW Automotive Electronics, the brake control; and Siemens, the assembly that manipulates the throttle. Last fall, Mercedes-Benz and Lexus joined the adaptive cruise control movement. Lexus offers an ACC option for its top-of-the-line LS430; at the movement, it is the only ACC system available in the United States. Mercedes’ system is an option on its C-Class and S-Class models, which are available in Europe.

This is an electronic system that allows the vehicle to slow while approaching another vehicle and accelerate again to the preset speed when traffic is cleared. It also warns the driver or applies brake if there is a high risk of a collision. In this project we are going to develop the micro controller based automatic vehicle speed control system. Now-a-days we can see that more number of accidents happens in highways. Most of the reason for accident is driver mistake. To avoid this situation we make the system which is called adaptive cruise control system. This system consist of Ultra sonic based obstacle detector, whenever it detect the obstacle automatically speed will be reduced, when the distance of the obstacle increases automatically speed gets increased. In this system, driver no needs to give the acceleration and also break, which is entirely controlled by system. Existing system is semi automatic and this system will be a fully automatic system.

Other interesting results can be found in the figure as well. Adaptive Cruise Control with full market penetration for passenger vehicles (VC3) shows the lowest capacities of all vehicle compositions with a lower value than the modeled reference alternative (VC2) and the theoretical CIA capacity similar to the straight road segments in previous section. However, it has a similar result as the default reference (VC1). Next, low to average market penetration of both Cooperative Adaptive Cruise Control (VC4 and VC5 respectively) as well as Autonomous Vehicles (VC7 and VC8 respectively) also show similar to reference capacities. This can be explained by the other share of these VC, since the rest of the market penetration of these VC are modeled as ACC. Apparently, the CACC needs to have a sufficient market share to significantly effect capacity. For low to average market penetration the magnitude of the effect on capacity is similar for both CACC and AV. However, full market penetration of CACC (VC6), seems to generate larger improvements on capacity than full market penetration of AV (VC9), since the latter one does not show any improvement on capacity in comparison to an average market share of AV (VC8). The combination of high vehicle automation and the cooperative aspect of driving combined appears to improve capacity at higher market penetration (VC11 and VC12). Similar to straight road segments, CAV is very robust to stochastic effects in traffic for an On-Ramp.

In many cases, driving simulator studies target how test persons interact with surround- ing traffic and with traffic signals. Traffic simulations like SUMO specialize in modeling traffic flow, which includes signal control. Consequently, driving and traffic simulation are coupled to benefit from the advantages of both. This means that all except the driven (ego) vehicle are controlled by the traffic simulation. Essential vehicle dynamics data are exchanged and applied frequently to make the test person interact with SUMO-generated traffic. Additionally, traffic lights are controlled by SUMO and transferred to the driving simulation. The system is used to evaluate an Adaptive Cruise Control (ACC) system, which considers current and future traffic light states. Measures include objective terms like traffic flow as well as the subjective judgement of the signal program, the ACC and the simulation environment.

The advancement in Artificial Intelligence field has spurred the inevitable advent of autonomous systems. Deep Learning has been instrumental in the notion of perception of world by machines. Whereas software algorithms have made it possible to crunch complex problems to mere few lines of code to make such autonomous system more efficient and fool proof. One such field of active research is Self-driving car. A self-driving car is junction of various systems like adaptive cruise control achieved with help of IR array, lane assists which uses proximity sensors, parking assistant with the use of computer vision and distance sensors, classification of obstacles by use of deep learning techniques. Commercially available self-driving cars make the use of 3D camera or LIDAR for better perception of world because along with visual graphics, it is able to gather depth information with the use of Point-Cloud [1]. More and more sensors are deployed in order to avoid the accidental scenarios and to make the system more reliable. In this thriving field of robust advancement, one approach that has been cited in this paper is of more software-logic based approach. It becomes objective when designing autonomous car for standard lanes as the features are definitive – black roads, white lines, yellow foot lanes. But when the environment changes to a more rural setting like University Road - it becomes a complex problem with very less identifiable features and the added noise. Simple Computer Vision technique render useless for such settings. To mitigate it, hard-coded algorithm is devised to control the car dynamics and it sits on top of AI algorithm that detects obstacles and further decision is taken as per the developed algorithm.