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`SAE TECHNICAL
`PAPER SERIES
`
`940904
`
`Radar Based Automotive Obstacle
`Detection System
`
`Walter Ulke, Rolf Adomat, and Karlheinz Butscher
`TEMIC GmbH
`
`Wolfgang Lauer
`Daimler Benz AG
`
`Reprinted from: Safety Technology
`(SP-1041)
`
`The Engineering Society
`For Advancing Mobility
`Land Sea Air and Space
`®
`I N T E R N A T I O N A L
`
`International Congress & Exposition
`Detroit, Michigan
`February 28-March 3, 1994
`
`400 Commonwealth Drive, Warrendale, PA 15096-0001 U.S.A. Tel: (412)776-4841 Fax:(412)776-5760
`
`1
`
`Mercedes-Benz USA, LLC, Petitioner - Ex. 1011
`
`

`
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`ISSN 0148-7191
`Copyright 1994 Society of Automotive Engineers, Inc.
`
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`
`940904
`
`Radar Based Automotive Obstacle
`Detection System
`Walter Ulke, Rolf Adomat, and Karlheinz Butscher
`TEMIC GmbH
`
`Wolfgang Lauer
`Daimler Benz AG
`
`Abstract
`This paper highlights the obstacle detection system
`developed by the Daimler Benz group to assist the
`driver, particularly in dangerous traffic situations and
`in case of bad weather.
`The area in front of the vehicle is scanned by a
`radar beam, and the actual traffic is analysed by
`signal processing software. Safety distance is
`calculated and displayed, and a warning is given to
`the driver whenever necessary. With this infor-
`mation, driving safety and comfort is increased
`while all actions to be taken still remain under driver
`control. Cruise control applications can be regarded
`as a natural system extension.
`A special chapter is dedicated to vehicle test data
`evaluation based on a mobile video system
`operating in the traffic environment and data post-
`processing in the lab.
`Effective synergy and a close co-operation of
`various companies resources made it possible to
`push this challenging project rapidly through
`different development stages to reach the volume
`pre-production phase in the near future.
`
`1. Introduction
`
`1.1 General motivation
`In consideration of the growing demand for
`increased driving safety, systems for environmental
`surveillance have been focused with particular
`interest in the recent years (/1/-/13/). Despite severe
`initial technological and commercial restrictions the
`automotive industry has been supporting these
`activities with growing interest and medium size
`volume applications can now be expected in the
`near future.
`
`These applications are motivated to a great extent
`by traffic accident statistics (Figure 1). Taking into
`account that frontal crash scenarios are greatly
`influenced by drivers reaction times it is a promising
`perspective to engage some sort of forward looking
`electronic aid.
`
`Fig. 1 Collision statistics
`
`This would allow faster response in critical situations
`which often emerge from driver distraction, fatigue or
`inattention. Figure 2 shows that about 60 % of the
`rear-end collisions can be avoided when driver
`reaction is advanced by a lead time of about half a
`second.
`
`41
`
`3
`
`

`
`I|n¢¢ndrwcI%iu2§H!§!!H§
`
`Fig. 2 Crash reduction by advanced driver reaction / 14/
`
`The ongoing discussion whether the driver should
`then be alanned by visible, audible or tactile means
`or should perhaps be fully bypassed by automatic
`interaction is beyond the scope of this paper, but
`nevertheless deserves specific attention.
`
`1.2 Systems for environmental
`surveillance and driver assistance
`
`Table 1 gives an overview of systems which
`combines environmental surveillance tasks with
`
`some sort of driver assistance. They all belong to
`the category of autonomous systems thus not
`having any data connection to other traffic members,
`however this is a likely future scenario.
`
`The present situation does not allow a final
`judgement whether one particular system will be
`dominant in the long run or if a combination of
`functions will eventually be grouped together in a
`common approach, joining near and short distance
`operation with obstacle detection and AICC. A
`similar uncertainty exists in the philosophy of driver
`alerting and data visualisation, and in the question of
`enhancing the drivei’s corrective measures in case
`of danger by automatic interactions to brakes and
`engine control
`is also not settled. Moreover the latter
`is an interesting issue with regard to product liability,
`especially in North America.
`
`From a system point of view, however, development
`work on these subjects has already started decades
`ago and is characterised to date by three competing
`principles for physical detection: millimetre wave
`(radar),
`infrared laser and image processing (video).
`
`System Task
`
`Obstacle warning with driver in the loop
`
`Automatic collision avoidance
`
`Sensor technology
`
`Radar/IR-LaserNideo
`
`Radarl|R-LaserNideo
`
`Intelligent Cruise Control (AICC) I Stop-Go
`
`Radarl|R-LaserNideo
`
`Blind spot and rear lane surveillance
`
`Radar/IR-LaserNideo
`
`Near distance waming systems (parking aids)
`
`U|trasoniclRadar/lRNideo
`
`Tab.
`
`1 Systems for Viromntal surveillance
`
`4
`
`

`
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`
`All of these rely to a high extent on sophisticated
`electronic sensor components in conjunction with
`fast digital processing capabilities. These
`technologies have been developed in the past with
`high effort, but without giving priority to the cost
`issues mandatory for automotive applications. It was
`the clear objective to introduce these technologies in
`defence and airborne products (including space)
`first. The automotive business was not of primary
`concern. The drastic changes in the global political
`climate have set free world-wide powerful capacities
`in high-tech companies striving now to reorientation
`on new markets. As automotive systems generally
`rely more on cost effectiveness and maximum
`simplicity than on absolute technological perfection,
`these high-tech incentives run the risk of being
`observed sceptically, at least when cost issues and
`consumer acceptance are concerned.
`Therefore it must be considered a strategic
`advantage when all required resources are
`
`combined and co-ordinated under the common roof
`of a large automotive player who attaches great
`importance to both driving safety issues and
`customer satisfaction.
`
`1.3 Daimler Benz Group synergetic co-
`operation programme
`With the background of this scenario, the concerted
`actions within the Daimler-Benz group are very
`promising because all required resources for system
`development, testing and production are in-house.
`Combined efforts are controlled by the automotive
`manufacturer himself (Mercedes Benz) with a clear
`perspective for volume introduction and customer
`relevance. This relationship is shown in Figure 3.
`
`Fig. 3 Synergy of resources in the Daimler Benz group
`
`43
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`Downloaded from SAE International by Eleanor Gonzalez, Monday, August 12, 2013 09:10:37 AM
`
`The main technical contribution from Mercedes
`Benz stems from its long term experience of
`automotive radar testing (infrared, microwave and
`video) which has been continuously practised over
`the last 20 years and which also laid the foundations
`for specific knowledge in traffic related software.
`All major system proposals to date have at least
`been investigated and realised on an experimental
`basis, including some very specific topics as for
`example systems for visibility detection in fog and
`rain.
`DASA know-how in radar design and radar data
`processing due to various activities in space and
`electronics (e.g. SAR) is ideally
`defence
`complemented by GaAs-microwave component
`research and manufacturing of MMlCs in the
`Daimler Benz research division.
`As far as cost effective automotive electronic design
`and development of operational software is
`concerned, TEMIC provides all resources for a
`successful partnership. And when it comes to
`production, TEMIC’s high volume manufacturing
`facilities are available at various locations
`worldwide.
`Although complete self supporting production is
`possible within the Daimler Benz group, a co-
`operation with external suppliers of radar front-ends
`is not out of scope.
`
`2. Radar Based Obstacle
`Detection System
`Among the various interesting approaches applying
`radar
`in
`the automotive environment,
`the
`development of a radar based obstacle detection
`and warning system has been pushed ahead by
`Mercedes Benz in the recent years with great
`emphasis, and the present status is determined by
`specific activities aligned to volume introduction. In
`the following, a technically oriented overview will be
`given of the system and its main constituents.
`
`2.1 General System Aspects
`
`2.1.1 System Philosophy
`The area in front of the radar equipped vehicle is
`scanned continuously by a radar beam and the
`actual traffic is analysed automatically on line by
`sophisticated signal processing software. Additional
`information of vehicle driving conditions is available
`from standard in-vehicle sensors, e. g. steering
`angle, speed, reverse driving and braking. Safety
`distance to a preceding vehicle is calculated and
`displayed, and a warning is given to the driver
`whenever a dangerous situation is detected,
`whether this is originated by moving or stationary
`obstacles on the road. With this information, driving
`safety and comfort is increased considerably while
`all actions to be taken still remain under driver
`control and responsibility. System operation is
`independent of weather conditions (fog, rain, snow)
`and day time (day, night). The warning should be
`given only if necessary to achieve highest advan-
`tage for the driver and to preserve him from irritating
`alerts and additional stress.
`According to the accident statistics, such a system
`has its main advantage on freeways and highways.
`Nevertheless system inherent false alarms, which
`could occur on small rural roads and in dense city
`traffic must be minimised.
`Cruise control is of course within the system
`capabilities but is not implemented in the first step.
`
`2.1.2 Technical Specification
`From the general system point of view, two radar
`operating principles can be distinguished for
`obstacle detection:
`
`- FM pulse doppler radar
`- FMCW radar
`In both cases electromagnetic waves are
`transmitted by a front-end antenna system and the
`reflected signal is used to determine target distance
`and speed. Multiple azimuth beams are required to
`detect stationary obstacles, vehicles driving in
`adjacent lanes and to track preceding vehicles on
`winding roads. The range of interesting targets is
`characterised by a large variation of radar cross
`sections which are a physical equivalent for signal
`reflection. A pedestrian, for example, with a cross
`section of about 1m2 must be detected as well as a
`truck driving nearby with a four decades higher
`cross section. This can only be achieved by a large
`signal processing dynamic range in combination with
`
`44
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`
`a sophisticated antenna design and low sidelobes of
`the radar beams.
`Although FM pulse doppler radars and FMCW
`radars show in many regards similar performance,
`the first was considered superior for the discussed
`system because of the simpler antenna design in a
`monostatic configuration and lower level microwave
`electronics involved. For instance, FM pulse doppler
`systems do not require high performance VCO-
`linearisation and the oscillator can be lower
`specified with respect to phase noise.
`The operating frequency band and output power is
`determined by FCC- regulations on the one hand,
`and on the other by the mechanical dimensions of
`the front-end, which becomes smaller at reduced
`wavelengths. This compromise is limited by
`microwave technology and available component
`production quantities. European systems are
`operated in the frequency band from 76 to 77 GHz
`and American regulations tend to legalise the same
`range.
`Table 2 shows an overview of important system
`parameters.
`
`based sensors are not influenced by fog or rain or
`snow. This is an inherent quality of radar and is also
`the main difference to infrared based approaches.
`The unit is packaged as a self-contained smart
`sensor, applicable in space critical vehicle head
`lamp constructions .
`An Electronic Control Unit (ECU) is dedicated to
`evaluation of front-end signals as well as further
`data processing for modelling and assessment of
`the actual traffic situation. To reduce cabling costs,
`the sensor front-end is controlled by the ECU via a
`custom designed serial link.
`A bi-directional CAN-BUS (Controller Area Network)
`is used to access vehicle specific information, e.g.
`steering angle or driving speed. Calculated safety
`distances, danger potential and warning signals are
`sent via the same CAN-bus to the Display and
`Warning Unit located on the dashboard
`
`Parameter
` Frequency
` Modulation
`Average output power
`Beam width
` Number of beams
` Range
`Distance resolution
`Velocity resolution
`Repetition rate
`
`Value
`77 GHz
` FM-Pulse
`200 µW
`ca. 3°
`3
`0 . . . 150m
`1m
`1 km/h
`20 Hz
`
`Tab. 2 Important system parameters
`
`2.1.3 Overall system block diagram
`The system consists of three functional modules
`(Figure 4) located differently in the car.
`The Radar Sensor Front-end can be called the
`"eye" of the obstacle detection system. In contrast to
`human eyes, the operating conditions of radar
`
`45
`
`Fig. 4 Overall system block diagram
`
`
`
`
`
`
`
`
`
`2.2 System Hardware Configuration
`
`
`
`2.2.1 Radar Sensor Front-end
`Radar sensor front-ends have been realised using
`different microwave design technologies. The
`conventional use of state of the art discrete
`semiconductor components gave very promising
`results in the first run, and could well be established
`in cost effective mass production, at least for the
`time being.
`Monolithic microwave integrated circuits (MMICs)
`are a very attractive long term alternative. However,
`manufacturing facilities require more sophistication
`and still do not operate at their cost targets. From
`the present point of view this can be achieved within
`the next years .
`
`7
`
`

`
`Figure 5 shows the general block diagram of the
`sensor front-end. The structure is quite simple and
`straightforward.
`
`Fig. 5 Sensor front-end block diagram
`
`Fig. 7 Sensor fiont-end prototype (Millitech)
`
`A more detailed circuit diagram of the microwave
`module is given in Figure 6 without further
`explanations.
`
`Fig. 6 Schematic of the 77 GHz GaAs-module
`
`Figure 7 gives a realistic impression of a front-end
`realisation in discrete technology whereas Figure 8
`shows a GaAs-MMIC-structure.
`
`Fig 8 MMIC front-end GaAs-module (DASA)
`
`8
`
`

`
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`
`distances are indicated on the instrumentation
`panel. Figure 10 shows a typical example of a
`display unit designed in a more traditional and well
`approved technique. However, new developments
`employing head-up displays look very promising and
`have a good chance to be applied, at least when
`cost issues improve.
`
`2.2.2 Electronic Control Unit
`The electronic control unit (ECU) operates as stand-
`alone equipment with interfaces to the radar front-
`end and to the display. High frequency signal
`conditioning of radar data, pre-processing and
`computing of warning algorithms are controlled and
`performed by a digital signal processor (DSP). The
`radar signal is amplified, mixed into the base band
`and digitised by an A/D-converter. To minimise cost,
`power consumption and space, the high frequency
`electronics are integrated into ASICs.
`A separate low-cost microcontroller is dedicated to
`input/output data handling via a CAN-BUS interface.
`An internal communication path between µC and
`DSP serves for data exchange and bilateral
`watchdog functions. Important diagnostic infor-
`mation and system parameters (e.g. calibration
`data) are stored in a non-volatile memory.
`Figure 9 shows the block diagram of the ECU.
`
`Fig. 10 Dashboard display unit (example)
`
`Fig. 9 ECU block diagram
`
`2.2.3 Display Unit
`When following a preceding vehicle the information
`about the actual distance in relationship to the safety
`distance is displayed on the dashboard. Since the
`absolute value in feet or meters is of no particular
`interest for the driver, only relative motions and
`
`2.3 System Software Configuration
`
`2.3.1 Radar data digital signal pre-
`processing
`To guarantee a short system reaction, a total cycle
`time of 50 milliseconds for DSP-calculations is
`specified. Figure 11 shows the details of data pre-
`processing.
`
`47
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`
`

`
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`
`Fig. 11 Timing of data pre-processing
`
`The pre-processing is performed sequentially for the
`three radar beams within 15 ms. Each beam is
`divided into thirty range gates which are treated
`separately. To calculate both distance and velocity
`information, a Fast Fourier Transformation of the
`radar signal samples is required. Pre-processing
`output data are distance, speed and amplitude of
`targets within the beams. Because of the multitarget
`capability, more than one obstacle in each beam
`can be recognised simultaneously. This array of
`radar data is treated as raw data.
`By inspection of the signal-to-noise ratio and
`averaging over a longer period of time, the detection
`limit is adjusted continuously. This technique results
`in an optimum likelihood of detection while
`simultaneously minimising the false alarms.
`A need for automatic system calibration arises from
`the fact that different types of cars require varying
`lengths of cabling between front-end and ECU.
`
`48
`
`2.3.2 Warning algorithm
`The warning algorithm is of crucial importance for
`system reliability and as a consequence, also for
`driver acceptance. Therefore the definition of
`adequate and powerful algorithms was one of the
`major tasks in the development phase. Their
`optimisation is still of topical interest .
`Figure 12 describes the warning algorithm in detail.
`
`10
`
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`
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`
`traffic scenario and to extract potentially dangerous
`situations.
`
`2.3.3 Warning Strategy
`The warning strategy depends on reference data
`which are stored in the ECU memory. Figure 13 for
`example gives an indication of how the safety
`distance is evaluated, taking into account reaction
`time and driving velocities.
`
`Fig. 12 Warning algorithm flow chart
`
`Generally speaking, so-called 'objects' are used for
`description of moving and stationary targets in front
`of the vehicle. By adjusting the parametric equations
`for object motion and acceleration, traffic members
`like cars, bicyclists or pedestrians are classified and
`their future behaviour can be predicted. This
`enables the warning algorithm to attach the
`incoming raw data to existing objects. As a first step
`new raw data are verified and then compared with
`already stored and tracked objects. New objects are
`generated for those parts of the raw data not fitting
`to the recorded history. In consequence the
`identified objects are assembled to model the actual
`
`Fig. 13 Calculation of safety distance
`Figure 14 shows the theoretical relations between
`initial speed vo, reaction time Tr and braking
`deceleration b. For example braking at vo=30m/s
`and Tr=1s on a dry road characterised by b=6m/s2
`results in a stopping distance of 105m.
`
`49
`
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`
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`
`Parameter
`
`b1 b2 Tr
`
`m 2
` s
` m/s2
`/s
`
`Road Surface DRY
`Road Surface WET
`Road Surface ICY
`
` 6,5
`6,5
` 2,6
`
` 6,0
`5,5
`2,0
`
`1,0
`1,0
`1,0
`
`Tab. 3 Different road conditions
`
`2.4 System Test Philosophy and
`R e s u l t s
`
`Test philosophy and practical data acquisition
`equipment is of utmost importance for gaining
`reliable information of system performance in a
`realistic traffic environment. The following shows
`examples of the procedures used.
`
`2.4.1 Testing strategy and equipment
`used on/off-road
`Figure 15 shows a typical selection of vehicles
`equipped with obstacle detection radar used for
`collection of on-road measurement data.
`
`Fig 14 Calculation of stopping distance
`
`Fig.15 Radar equipped vehicles for test drives
`
`Vehicle test data evaluation is based on a mobile
`video system operating in the traffic environment
`and data post-processing in the lab. Thereby it is
`possible to reproduce realistic driving conditions on
`the workbench for simulation purposes and software
`optimisation (Figure 16 on next page).
`Totally covered mileage is about 300000 km to now
`and testing is still underway.
`
`50
`
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`
`Fig. 16 Data acquistion and test philosophy
`
`2.4.2 Test results
`A typical impression of radar based obstacle
`detection performance is presented using a series of
`pictures which illustrate the process of compression
`of information. The example starts with a picture of a
`typical traffic situation and ends with the output of
`the test display that was used in the development
`phase.
`A passing situation is selected as a critical example
`since the system has to decide whether the
`bypassed vehicle is on the same lane or not. Figure
`17 is a video hard-copy of the scene just before
`passing a truck driving on the right lane.
`
`Fig. 17 Video picture of the passing situation
`
`5 1
`
`13
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`
`Three vehicles are detected as shown in Figure 18.
`
`The right beam receives the reflection from the truck
`on the right lane. The left and centre beam observe
`the preceding passenger car on the same lane. This
`vehicle is tracked by the warning algorithm and
`chosen as the proper one for safety distance
`measurement. The second truck on the right lane
`appears in the center beam at higher distance and is
`of no further interest at that moment
`
`Fig. 18 Raw data of the passing situation
`
`To check out the radar system during the
`development phase a specific display was used in
`the test car as shown in Figure 19. This display
`gives a compact impression of the actual system
`performance and its reconstruction of the traffic
`scenario. Moving targets are indicated with a car
`icon. The colour of the icon codes the relative
`velocity.
`Although it was found that this type of display is of
`great help for technically oriented people, it might
`confuse the average driver with to much information.
`
`Fig. 19 Display for development assistance
`
`3. Summary
`This paper has reviewed a particular development
`carried out by the Daimler Benz group with the
`objective to introduce a radar based system for
`obstacle detection and warning at an attractive price
`level .
`As an essential prerequisite for meeting customer
`requirements, a co-operation between car
`manufacturer and electronic suppliers was used
`from the very beginning of system design work. The
`rapidly advancing progress at the GaAs component
`level was a considerable factor for providing cost-
`effective sensor front-ends, which constitute a major
`part of the overall system cost.
`Prospects are very encouraging that user demands
`for increased driving safety can be met and further
`system enhancements, such as cruise control be
`implemented without significant new difficulties to be
`solved.
`
`52
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`14
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`

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`/12/ Stove, A. G.: "Automobile Radar", Applied
`Microwave USA, 1993, Vol. 5, No. 2, p.102-
`115
`/13/ Stove, A. G.., Mallinson, P.: "Car Obstacle
`Avoidance Radar at 94 GHz", 7th Int. Conf. on
`Auto. Electronics, London 1989, p. 297 - 302
`/14/ Enke, K.: "Möglichkeiten zur Verbesserung
`der aktiven Sicherheit innerhalb des
`Regelkreises Fahrer-Fahrzeug-Umgebung",
`7.lnternationale Technische Konferenz über
`Experimentier-Sicherheitsfahrzeuge, Paris
`1979
`
`Acknowledgement
`The authors gratefully acknowledge Mercedes Benz
`and Daimler Benz, in particular senior vice president
`Mr. Gaus, for their co-operative work. We would also
`like to thank our colleagues of DASA Deutsche
`Aerospace, who helped to make the system a
`success.
`
`Literature overview
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`Lowbridge, P.: "A 77 GHz Collision Avoidance
`Radar for Automotive Applications", 8eme
`Congres Int. SIA - Fiev - Equip’Auto, 25.-
`27.Oct 1993
`Hahlganss, G., Hahn, I.: "Headway Radar
`Using Pulse Techniques", IEE Int. Conf. on
`Auto. Electronics, Publ. No. Vol. 141, London
`1976, p.132 - 135
`Wüchner, E.: "Anti-Collision Radar Making
`Progress", Automotive Engineering, July
`1978, p.78-80
`Kiyoto, M., Fujiki, N., Fujiwara, T.: "A Study of
`the Automatic Braking System", 7th Int. Tech.
`Conf. on Exp. Saf. Veh.", Paris 1979
`Belohoubek E., et. al.: "Radar-Controlled
`Functions in the U.S. Research Safety
`Vehicle", 7th Int. Tech. Conf. on Exp. Saf.
`Veh.", Paris 1979
`Troll, W. C., Wong R. E., Wu Y. K.: "Results
`from a Collisions Avoidance Radar Braking
`System Investigation", SAE Int. Auto. Eng.
`Congress, Detroit 1977
`Tamara, T., Twabe, A., Ban, K.,: "Radar
`Sensor for Automotive Collision Prevention",
`IEEE MTT-S Int. Microwave Symposium
`Digest, 1978, p.168 - 170
`Nogami, T., Sakamoto, T.: "Automotive
`Warning System Using 50 GHz Band Radar",
`9th Int. Tech. Conf. on Exp. Saf. Veh., Kyoto
`1982, p.917-923
`Bucher, A., Grünbeck, W., Pischke, J.,
`Wocher, B.: "Ein Abstandswarngerät für
`Kraftfahrzeuge in 35 GHz Impulstechnik",
`Mikrowellen-Magazin Nr. 8, Heft 4, 1982,
`p.426 - 429
`Wirbitzky, G.: "Unfallverhütung durch Radar-
`Abstandswarngeräte", Nahverkehrspraxis Nr.
`31, 1983, p.324-326
`Lichtenberg, C. L.: "Application of Radar for
`Automotive Crash Avoidance", SAE-Paper
`870496, 1987, p.79-85
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