Handfree Driving For Automobiles

          This seminar paper is based upon the project work being carried out by the collaboration of Delphi-Delco Electronics (DDE) and General Motors Corporation. It was named the Automotive Collision Avoidance Systems (ACAS) field operation program to build the tomorrow’s car. It used latest technologies of radar sensing to prevent collision. Video imaging to track its path, and uses DGPS for locating the position of the vehicle on the road. It completely utilized the latest technologies in Robotics as obstacle sensing, tracking and identification.

 All of us would like to drive our car with a mobile held in one hand, talking to the other person. But we should be careful; we don’t know when the car just before us applies the break and everything is gone.  A serious problem encountered in most of the cities, National Highways, where any mistake means no ‘turning back’! There comes the tomorrows technology; Hand free driven car. Utilizing the modern technological approach in Robotics.
All around the world almost 45% of the accidents occur by mistakes of the driver. In some cases the driver is engaged in some other affair than driving. In USA the highways are so crowded that in some situations mistake on the part of one person on the road can lead to serious accidents. Most of these accidents are fatal. One such accident took place in the year 1997, on a foggy morning the on a heavily traffic highway a series of collisions took place in which 5 lost their life and more than 40 injured.  The victims of such accidents are either severely injured, some even risk their life by their careless driving. This was the main reason behind this project work put forward by the Delphi-Delco electronic systems and General Motors Corporation. It was called the Automotive Collision Avoidance Systems (ACAS) field operation program.

It is the Automotive Collision Avoidance System (ACAS). The ACAS/FOT Program has assembled a highly focused technical activity with the goal of developing a comprehensive FCW system that is seamlessly integrated into the vehicle infrastructure.. The FCW system incorporates the combined ACC & rear-ends CW functionality. The ACC feature will only be operational when engaged by the driver. On the other hand, the FCW feature will provide full-time operating functionality whenever the host vehicle is in use (above a certain min speed). This feature is effective in detecting, assessing, and alerting the driver of potential hazard conditions associated with rear-end crash events in the forward region of the host vehicle. This is accomplished by implementing an expandable system architecture that uses a combination of: (a) a long range forward radar-based sensor that is capable of detecting and tracking vehicular traffic, and (b) a forward vision-based sensor which detects and tracks lanes. The proposed program effort is focused on providing warnings to the driver, rather than taking active control of the vehicle.
Due to the complexity and breadth of the system goals, the on-going design process has heavily relied on using the established principles of system engineering as a framework to guide this highly focused deployment design effort. As such, the technical activities of the program can be grouped into four main activities within two phases. Phase I started immediately after program inception in, June 1999, and lasted approximately 27 months. Phase II started immediately after the end of Phase I. The objective was that the two program phases will be continuous with minimal disruption of program flow and continuity between them. Consequently, activities that enable the continuous workflow into Phase II will be initiated during Phase I. The program phases are summarized as:

Phase I
1. Development - The program initially focused on a variety of activities associated with the enhancement, improvement, and maturation processes applied to existing FCW technologies/components that were developed during the ACAS Program, while accelerating the development of other key subsystems,
2. Integration - The refined FCW portfolio of technologies/components was upwardly integrated into the vehicle platform infrastructure to form a comprehensive rear-end collision warning system,
Phase II
3. Deployment Fleet - The validated design was used to build a deployment fleet of ten vehicles equipped with the system; and
4. Field Operational Test - The culmination of this program activity will be the design and implementation of the FOT plan. The deployment vehicle fleet will be used to collect valuable market research data in order to assess/validate the technology, product maturity, and general public perception.
           The FOT is the natural next step of the technology development cycle that was initiated with the Automotive Collision Avoidance System (ACAS) Development Program. This program was sponsored through the Technology Reinvestment Project (TRP) and administered by the National Highway Traffic Safety Administration (NHTSA) between January 1995 and October 1997. Delphi-Delco Electronics Systems (DDE) and General Motors (GM) were major participants of the eight-member ACAS Consortium. Additionally, DDE led the ACAS Consortium. The primary objective of the ACAS Program was to accelerate the commercial availability of key collision warning countermeasure technologies, through either improved manufacturing processes or accelerated technology development activities. The next logical technical progression of the product development cycle was the upward integration of these ACAS-developed essential building blocks to form a complete seamless vehicle system that will be evaluated through a field operational test program. It is apparent that the introduction of Adaptive Cruise Control (ACC) systems is imminent. Therefore, posing the notion of a field operational test of the collision warning technology at this time is apropos. An extensive, comprehensive collision warning FOT has never been undertaken in the United States (or anywhere else for that matter). As such, very few studies exist which adequately understand the relationship between system performance capability, user acceptance, and safety benefits based on involvement by the general driving public. This test program provides an ideal opportunity for the Government, industry, and ITS community to gain a more thorough understanding of the requirements, functions and societal impact of this technology. Additionally, any potential adverse operational and safety-related issues could be identified, analyzed, and addressed while the technology is still in the early stages of product development. This program has the opportunity to make a positive contribution in the development of this technology.
        In support of achieving a successful field operational test, the ACAS/FOT Program had assembled a highly focused technical activity with the goal of developing a comprehensive FCW system that was seamlessly integrated into the vehicle infrastructure. The performance of the cohesive collision warning vehicle package will be of sufficient fidelity, robustness, and maturity so that a meaningful field operational test program can be executed. The FCW system will incorporate the combined ACC & rear-end CW functionality. The ACC feature will only be operational when engaged by the driver. On the other hand, the FCW feature will provide full-time operating functionality whenever the host vehicle is in use (above a certain minimum speed). This feature will be effective in detecting, assessing, and alerting the driver of potential hazard conditions associated with rear-end crash events in the forward region of the host vehicle.

         It is the Forward Collision Warning system. It was one of the earlier programs done by the DDE systems. It included the forward looking radar sensing system. It is high range forward looking radar. Forewarn Smart Cruise Control with Headway Alert uses a mechanically-scanning, 76 GHz, long range radar sensor to detect objects in the vehicle’s path upto 150 meters or 402 feet ahead. The system helps to reduce the need for drivers to manually adjust speed or disengage cruise control when encountering slower traffic.
        Using the vehicle's braking and throttle systems, Smart Cruise Control with Headway Alert automatically manages vehicle speed to maintain a time gap (following distance) set by the driver. Driver information displays indicate the cruising speed and driver-selected gap. The system can also alert drivers when slower traffic is detected in the vehicle’s path. When the Headway Alert system is active, audible and visual alerts warn the driver when braking is necessary to avoid slower moving vehicles ahead. Drivers can adjust system sensitivity to their preferred driving style.
        During the past few years, DDE has modified three of its engineering development vehicles that are being used to support the ACAS FOT Program. These vehicles are: (a) 1994 Toyota Lexus LS400, (b) 1994 GM Cadillac Seville, and (c) 1998 Opel Vectra. These vehicles have been modified to provide the basic functionality of fully integrated ACC and FCW systems.

     It is the Adaptive Cruise Control system.  In the current ACAS FOT program, four complementary host and road state estimation approaches are being developed. The complementary approaches are as follows:
 (a) vision based road prediction
(b) GPS based road prediction
(c) radar based scene tracking
(d) yaw rate based road and host state estimation
These four roads and host state estimation approaches are being correlated and fused by the Data Fusion system and provided parametrically to the Tracking and Identification Task. The fused road and host state information provides an improved estimate of the roadway shape/geometry in the region ahead of the Host vehicle, and an improved estimate of the Host vehicle’s lateral position and heading within its own lane. This information is being incorporated into the Tracking and Identification functions to provide more robust roadside object discrimination and improved performance at long range, during lane change maneuvers, and during road transitions. In addition, a new radar-based roadside object discrimination algorithm is also being developed to cluster and group roadside stationary objects, and the first generation truck discrimination algorithms developed during the previous ACAS program are being enhanced. Furthermore, a new yaw rate based host lane change detection algorithm is also being developed.

       The overall goal of the Forward Vision Sensor is to facilitate the development of a robust, real-time forward looking lane tracking system to enhance the overall forward Path Estimation and Target Selection algorithms. The system consists of two components. A video camera, mounted behind the windshield of the vehicle, will acquire images of the roadway ahead of the host. A remotely located image processing unit will then detect and track the position of the lane boundaries in the images, and will provide a model of the changing road geometry. In addition to road shape, the lane tracking system will provide estimates of lane width and of the host's heading and lateral position in the lane. In the Data Fusion Module this information will be fused with road and host data from other sources, such as Scene Tracking and GPS Map, to provide more accurate estimates of road and host state to the Target Selection Module.

forward vision camera
        Although many different vision-based lane detection and tracking systems have been developed worldwide, their primary focus has been on applications such as lane departure warning and lane keeping, where the required range of operation is usually less than 25 meters. Host heading and lateral lane position derived from such systems can be used to reduce the effects of driver hunting and host lane changes on the task of in-path target selection, but the more serious problems associated with curve entry/exit scenarios remain. To address these, an accurate prediction of the roadway geometry up to 100 meters ahead of the host is desired. The goal of this task was to develop a vision-based lane tracking system that will provide these long-range road curvature estimates as well as complement the Scene Tracking and GPS approaches under development in Tracking and Identification Task.
         To develop the robust vision system required for this program, and to take advantage of existing automotive vision technology, three short-range real-time lane tracking systems were identified as potential starting points for this task. Selection of these systems was based on their developer's demonstrated competency in the development, integration, and road testing of these systems, and on their willingness to extend their system to meet the goals of this program. Teams from the University of Pennsylvania (U-Penn), Ohio State University (OSU), and the University of Michigan – Dearborn (UM-D) were each contracted by DDE1 to further the development of their respective systems.
        The NHSTA states certain requirements for the system which analyze the road. The requirements state that the system should provide host and road state estimates to within these specified one-sigma accuracy requirements:

1. Lateral position in lane: < 0.2 meters
2. Lane width: < 0.2 meters
3. Heading: < 0.2°
4. Road Geometry: < 0.75 meters at 75 meter range2
     The Forward Vision Sensor should produce confidence estimates (which may be a function of range) for the road-geometry and host vehicle state. The system should also report the number of lane markers (i.e. left, right or none) that it has acquired as well as some indication of when a lane change event has occurred. The minimum update rate is 10 Hz with an initial maximum acquisition time of 5 seconds. The system should work on the freeways, freeway transitions, expressways and parkways where the minimum horizontal radius of curvature is 300 meters, and when the host speed is between 25 and 75 mph. The system will operate in clement weather, in both day and night conditions, and under natural and artificial lighting. The road surface should be paved, clear, and free from glare, and the road markings should have good contrast. The lane markings can be of single or double lines that are either solid or dashed. A Vision EDV was configured as a test bed for the development and evaluation of the lane tracking systems. GM supplied a 1996 Buick which was outfitted by DDE with a CCD-camera, CAN bus, speed and yaw rate sensors, a vehicle interface processor to format and transmit the vehicle data on the CAN bus, and the video encoder system described above. This vehicle was provided for the shared use of all vision teams, and has been driven by each to collect the video scenarios that are currently being used for system refinement and validation. During the down-select process, each of the vision systems can be integrated into the vehicle, and data collected from each simultaneously. It requires a 233MHz Pentium MMX processor to process these data collected from the sensors.

Delphi Diagnostic tracking and identification display
       During the first year of the program, enhancements to the target selection algorithms were developed to improve performance during curve transitions and host lane changes. Modifications were made to compute target lateral lane positions using the road and Host state derived from the radar based scene tracking sub-system, and to use this information to better distinguish between in-lane and adjacent-lane vehicles. Improvements were also made to shift the target selection zone to the adjacent lane during host lane changes, and to alter the zone’s characteristics while the host is settling into the new lane. Prior to the start of the ACAS FOT program, Delphi began an effort to develop and evaluate alternative host lane change classifiers. The classifiers were designed to satisfy the requirements that (a) lane-change must be detected before it is approximately 50% complete, and that (b) the cost of false lane-change detections is very high. A variety of neural network classifiers, decision-tree classifiers, and individual template-matching classifiers were constructed. In addition, ensemble classifiers consisting of various combinations of these individual classifiers were also been constructed. The inputs to each classifier have included various combinations of yaw-rate data, heading angle data, and lateral displacement data; the outputs denote whether the host vehicle is currently making a left lane-change, a right lane-change, or being driven in-lane.
           During the past year, refinements have been made to the core host lane change detection algorithms. Thus far, an ensemble classifier consisting of three neural networks has shown the most promise. Tests on a very limited amount of data suggest that this classifier can detect approximately 50% of the lane-changes made while generating on the order of 5-10 false alarms per hour of driving. Delco is continuing to look at techniques for improving this performance. In addition, the neural network ensemble classifier is currently being incorporated into the target tracking and identification simulation.
           In past years, an effort was initiated to detect roadside distributed stopped objects (DSOs) using various linear and curve fit approaches. Examples of such a distributed stopped object are a guardrail, a row of parked vehicles, a row of fence posts, etc. Two advantages of having this information are that it allows: (a) discrimination of false targets from real targets during curve transitions and pre-curve straight segments, and during host lane changes; and (b) utilization of the geometry of the distributed stopped object to aid in predicting curves in region ahead of the host vehicle.
           This task is still in the very earliest stages of development. Several algorithms have been tried, with varying results. Much of the work has focused on finding useful ways to separate radar returns associated with DSOs from the other stopped object returns. In the early algorithms, it has been assumed that distributed stopped objects will provide returns that form a distinguishable line. The focus of these algorithms has been to find the line amid all of the stationary object returns. Other algorithm efforts have concentrated on defining the geometry of the DSO to aid in predicting the location of the road edge. Figure.2 shows an example of Delphi’s DSO clustering approach. The figure depicts stopped objects taken from a single frame of data that was collected with the HEM ACC2 radar during a road test. The circles in the figure represent stopped object returns that were seen for the first time in the current frame. The squares represent "persistent" stopped object returns (i.e.: returns that have appeared on enough successive scans to be considered real objects). The triangles represent formerly persistent radar returns that have disappeared momentarily and are being "coasted" by the radar tracker. Color-coding of the objects is used to denote radar track stage of each return.

the Host Vehicle is on a road with a guardrail on the right side, approaching a left turn, and will then encounter a T-intersection. Some cars are parked along the other road. The algorithm was able to detect the guardrail and not be distracted by the parked cars. DDE has developed a Matlab™ based road scenario generator that propels the host and various scene targets along different predefined road scenarios. The model includes a host steering controller, radar model, and yaw rate and speed sensor models. The scenario generator also allows host and target weaving and lane change behavior to be specified. These simulated scenarios are used to evaluate the Target Tracking and Identification algorithms.

          In the current ACAS program path prediction is achieved by continuously estimating the location of the vehicle on the road, matching the vehicle location to a point on a road in the stored roadway map, tracking the path traversed by the vehicle and extracting the upcoming road geometry from the map. The objectives of this task are met using several sensors such as DGPS, dead reckoning and a digitized road map.
                    DGPS is used to compute the heading and distance traversed by the vehicle. The accuracy in determining the heading and distance is further enhanced by computing the heading angle and distance relative to the previous position of the host vehicle. Apart from the benefits that DGPS based systems offer, they are seriously plagued by outages in GPS signals that occur in the presence of tunnels and tall buildings, among other things. In order to overcome this shortcoming, the developed approach is augmented with dead reckoning sensors, where wheel speed sensors and odometer are used for distance measurement and yaw rate sensor, compass and differential wheel sensors are used for angle measurement.
         The combination of dead reckoning and DGPS with the map database has been explored to obtain a map based path prediction system. DGPS, when used in conjunction with the map database, can provide fairly accurate path prediction except in situations of GPS signal outages. At such times, the dead reckoning is expected to carry forward the task of path prediction.
         The above discussion has assumed the availability of accurate map database (a major component of the discussed system) that meets the design specifications. It should be noted that such a database is not commercially available at the present time. Within the limited scope of the ACAS-FOT project, AssistWare (fig.4) has been contracted to aid in the development of maps that are superior to those commercially available.

         The most important part of the ACC system is the Digitized GPS .Global Positioning Satellite Systems (GPS) are navigation tools which allow users to determine their location anywhere in the world at any time of the day. GPS systems use a network of 24 satellites to establish the position of individual users. Originally developed by the military, GPS is now widely utilized by commercial users and private citizens. GPS was originally designed to aid in navigation across large spaces or through unfamiliar territory. As a tool for law enforcement, GPS can assist agencies by increasing officer safety and efficiency.
         The United States Coast Guard defines GPS as "a satellite-based radio-navigation system." In lay person terms, GPS operates when a network of satellites "read" the signal sent by a user’s unit (which emits a radio signal). A GPS unit receives data transmitted from satellites— at least three satellite data inputs are necessary for accurate measurements.
          The unit then interprets the data providing information on longitude, latitude, and altitude. GPS satellites also transmit time to the hundredth of a second as coordinated with the atomic clock. With these parameters of data and constant reception of GPS signals, the GPS unit can also provide information on velocity, bearing, direction, and track of movement.
        GPS receivers can be integrated with other systems, such as a transponder or transmitter. The transmitter takes information from the GPS receiver and transmits it to a defined station, such as a police dispatcher. The dispatcher must have the system to both receive the transmission in "real time" along with the GPS data. To be truly useful, this information must be integrated with a Geographic Information System (GIS) which has a map of the community and translates the longitude and latitude into addresses.

Brake Control System
       A new Delphi Brake Control System will replace the OEM brake components on the Prototype and FOT deployment vehicles. The brake control system includes an anti-lock brake system (ABS), vehicle stability enhancement, and traction control features. For this program, the brake system will be enhanced to respond to ACC braking commands while maintaining the braking features and functions that were in the original brake system. Delphi’s common best engineering practices will be used to perform safety analysis and vehicle level verification of the brake system to ensure production-level confidence in the brake system.
Over the past two years, the DBC 7.2 brake control system has undergone significant testing for production programs. During the first year of the ACAS/FOT program, the brake system was integrated on a chassis mule and one of the Engineering Development Vehicles. Calibration and tuning of the brake system has started.

Throttle Control System
       The throttle control system maintains the vehicle speed in response to the speed set by the driver or in response to the speed requested by the ACC function. The Delphi stepper motor cruise control (SMCC), standard in the Buick LeSabre, will be modified to perform the required functions. The required modifications have been used successfully in other projects. During the first year, interface requirements were defined and throttle control system modifications were designed for the prototype vehicle.
      The information from the ACC controller and the ACC radar sub-system is fed to the processor through the CAN bus which has the data rate of 500kbps. The ACC controller controls the throttle and brake actuators to have effective brake and throttle control. The block diagram fig.5 given below shows the ACC system.
   The primary ACC Subsystem display will be in a head-up display. The primary ACC display will include the following information:
a. ACC On/Off
b. Set Speed
c. Current Speed
d. Tracking/Not Tracking a Lead Vehicle
e. ACC Operational/Failed
                    The vehicle will provide a forward collision warning capability that will provide alerts and advisory displays to assist drivers in avoiding or reducing the severity of crashes involving the equipped vehicle striking the rear-end of another motor vehicle. For the purposes of the FOT, the FCW will have enabled and disabled modes. The FCW will be enabled and disabled when conditions specified by the ACAS/FOT engineers are met using the same mechanism that enables and disables the adaptive capability of the cruise control. The driver will not be able to disable the FCW, but the driver will be provided with a control to adjust the sensitivity (alert range) of the FCW function. The sensitivity adjustment will not permit the FCW function to be disabled by the vehicle operator.
        The FCW algorithms (Table below) depend upon whether the ACC is active. The ACC is considered to be active in the Maintain Speed or Maintain Headway modes.

FCW Modes
FCW with ACC Inactive In this mode the FCW does not expect the ACC to provide any braking.
FCW with ACC Active In this mode the FCW warning algorithm takes into account the braking function that the ACC can provide. An alert is produced if the ACC braking authority is inadequate to prevent a collision.

Cruise Control Modes-Adaptive Cruise Control Enabled
The cruise control behaves like a standard cruise control system until the adaptive features are enabled . the states and transitions for the cruise control when the adaptive features are enabled.
ACC Off The ACC system is not functional. This state is entered whenever the ignition is on and the ACC is turned off.
Standby without speed set
The system is waiting to take control of the throttle and brakes. This state is entered when the ignition is turned on and the ACC is turned on. From this state the system can be activated by pressing the set button after the vehicle has reached the minimum set speed.
Standby with speed set
The system is waiting to take control of the throttle and brakes. A set speed has been established previous.
Maintaining Speed In this mode the ACC system attempts to reach and hold a specified speed. While in this mode the set speed can be increased or decreased by pushing or tapping on the resume/accel or set/coast buttons.

Maintaining Headway In this mode the ACC system attempts to reach and hold a specified headway. While in this mode the set speed can be increased or decreased by pushing or tapping on the resume/accel or set/coast buttons.

Manual Throttle Override In this mode the driver is pushing on the throttle to force the vehicle to go faster than the cruise control function would command.

Under Minimum Speed While Active In this mode the ACC has reduced the vehicle speed below a minimum cruise speed because a slow vehicle is ahead. Once this happens the ACC will not cause the vehicle to accelerate. When this state is entered the driver is given a message to take control of the vehicle.


  • Conveniently manages vehicle speed and headway gap
  • Complements vehicle styling
  • Makes cruise control more useable in most traffic conditions resulting in a more relaxed driving experience.
  • Operates under wide range of environmental conditions(dirt, ice, day, night, rain, or fog)
  • Low false alarm rate


  • Radar-based sensing for optimal performance
  • Sensor hidden behind front grille or fascia
  • Best available detection and tracking performance
  • Manages vehicle speed and headway gap using throttle control and limited braking
  • Automatically notifies driver of a blocked sensor via displayed message
  • Excellent following distance and speed control

          Robotics is the part of electronics engineering, which exploits each aspect of electronics and mechanical engineering. The developments of robotics have lead to the ACAS program. On the completion of this program vehicles will change the phase of driving; a handfree driving.

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