ANALYSIS OF LIDAR SENSORS FOR NEW ADAS

APPLICATIONS. USABILITY IN MOVING OBSTACLES

DETECTION.

Authors: F. García

1*, F. Jiménez

2, J.E. Naranjo

3, J.G. Zato

3, F. Aparício

2, J.M.

Armingol1, A. de la Escalera

1.

1 Universidad Carlos III de Madrid. Laboratorio de Sistemas Inteligentes

Avda. De La Universidad 30, 28911 Leganés (Madrid). Spain. Telephone: +34 91 624 99 70. E-mail: fegarcia@ing.uc3m.es

2 Universidad Politécnica de Madrid. E.T.S.I. Industriales. INSIA

Carretera de Valencia, km.7, 28031 Madrid. Spain. 3 Universidad Politécnica de Madrid. E.U. de Informática

Carretera de Valencia, km.7, 28031 Madrid. Spain.

ABSTRACT

ADAS applications feasibility is connected with the necessity of trustable sensors. The lack

of cheap and reliable sensors underlines the need to use different sensors at the same time.

LIDAR provides reliable but limited information of the surroundings for a vehicle

application. This paper presents a comparison between two different kinds of LIDAR

sensors focusing on their possibilities of being used in ADAS applications. Finally a new

method for detecting moving obstacles, mainly vehicles, is proposed. This method has been

implemented and tested; results of the different test performed are shown.

KEYWORDS

Data Fusion, Intelligent vehicles, ADAS, LIDAR

1.- INTRODUCTION

ADAS applications feasibility is connected with the necessity of trustable sensors. In this

context, the lack of cheap and reliable sensors underlines the need to use different sensors

at the same time in order to provide a reliable and accurate application. A possible set of

sensors used in data fusion applications are computer vision and LIDAR. The reason for

using these sensors is that LIDAR provides a reliable source of possible detections in the

surroundings; on the other hand, data provided by vision sensors allow the different objects

detected by the LIDAR to be classified. This paper focuses on the necessity of a reliable

LIDAR sensor, able to detect the surroundings obstacles and even been able to give a first

estimation of the shape of the detected obstacle.

Two different LIDAR have been tested in different conditions and movements to provide a

comparison of their possibilities for ADAS applications. Typically, LIDAR are used to

provide detection, but not usually give an estimation of the kind of obstacle that is detected.

A novel method obstacle detection and classification is proposed, this classification focus

on the most important obstacles that can be found in a road environment, pedestrian and

vehicles. These obstacles represent other users of the road, which detection and tracking are

crucial for developing safety applications. This information can be lately used in a fusion

application together with visual sensors to perform a more accurate classification.

Radar researches are widely used in applications for road environments. In [1] frequency

laser radar are used to classify vehicles according to their height, [2], [3], [4] and [5]

combines frequency radar applications and other sensors like visual information.

In the latest years, LIDARs are becoming more popular in road environment applications,

[6] performs classifications and tracking based in different possibilities for each detected

obstacle. [7] and [8] detects and classificates pedestrians based on the movement of the

obstacle. [9] detects potentially dangerous zones in the road where pedestrians are more

vulnerable by computing several sequences and with obstacle detection correlation. [10]

and [11] Detects and classifies based on the shape and movement.

Other ADAS application which does not involve obstacle classification can be developed

using laser range sensors [12] and [13].

2.- OBJETIVES AND METODOLOGY

The following objectives have been considered:

- Compare the detection using two different sensors and establish the detection and

identification limits for each sensor studied. The following equipment was tested LD

LRS 1000 and LMS-291 both of them provided by SICK. Both of them have different

qualities and belong to two different LIDAR families.

These tests were performed in a controlled environment which could be configured

specifically for these tests. And the vehicles that were used were a metallic-grey

Peugeot 307 and a black Nissan Note (Figure 1). The first vehicle represents the best

case scenario were the reflectivity is high, thus no losses due lack of reflectivity can be

found. The second vehicle is the worst case scenario; its black paint represents the most

challenging situation for the laser range finder due its lower reflectivity. So in this test,

not only a comparison between two families of laser range finder LIDARs are

performed, also a the viability of the laser radars in ADAS applications are tested in the

worst case scenarios.

Figure 1. Test vehicle with radars mounted in the bumper (center), black colored car

(left) and gray colored car (right).

- A novel approach to vehicle and pedestrian detection in road environments presented in

[14] and tested in different conditions, results are shown.

3.- LIDAR COMPARISION

The sensors used are both 2D LIDAR from SICK. They were mounted in the bumper of a

vehicle to perform several test sequences in order to compare the capabilities of each sensor

(Figure 2). Before the main part of the test is explained, the main characteristics of each

sensor are presented.

LRS-1000

The data acquisition frequency of the LRS-1000 can be selected from 5 to 10 Hz. Its

maximum distance measured is up to 250 meters and the resolution can be selected from

0.125 to 1.5º. The LSR-1000 is a high profile laser measurement system, its wide field of

vision, up to 360º, its high maximum distance and lower resolution makes it a very

interesting tool for ADAS application. The main disadvantage that can be found in this part

is the lower frequency; it gives a scan every 100 msecs.

LMS-291

Can be configured with a frequency up to 76 Hz. but for the selected resolution (0.25º) its

maximum operating frequency is set to 19Hz The resolution can be selected from 0.25 to

1º. And its maximum detection distance is 80m. The main characteristic is its high

frequency, it gives information every 52msecs. In ADAS applications, real time detection is

a critical point, thus the faster the detection the more suitable the sensor for these

applications.

Figure 2. LMS 291 (left) and LSR 1000 Laser Radar (rigth).

Table 1. A priori comparison of laser performance.

LRS-1000 LMS-291

Field of Vision 360 180º/100º

Resolution 0.125º to 1.5º 0.25º to 1º

Max Distance measure 250m 80m

Detection Distance(1)

229,2 m >80 m

Max Distance(2)

114.6 m 57.3 m

Working frequency 10Hz 19Hz

(1) At least four detection point, from the formula:

22 mintg

ddist , for a car with 2 meters width means

d=0.5m (distance between points). (2)

It has been estimated that 8 points is the minimum amount of points necessary to give a good estimation.

For a 2 meters width car, d=0.25.

In order to evaluate the accuracy of the measurements, different Tests have been

performed. Tested movements are shown in figure 3.

Figure 3. Test performed. Separating movement A, Approaching movement B and

crossing movement C.

In each movement, detection widths for the moving vehicle were recorded, as well as the

number of detected points that defines the car. Results are shown in figure 4 to 8:

Figure 4. Width measure for LRS-1000 movements A and B in millimeters.

Figure 5. Width measure for LMS 291

movements A and B in millimeters.

Figure 6. Pulses detected for LRS-1000

movements A and B.

0

20

40

60

80

5 15 25 40 60 80 100 120

Distance detected [m]

Width[m]

in

meter

s

Width[m]

in

meter

s

Distance detected [m]

in meters

# of points detected

in

meters

Distance detected [m]

in meters

Figure 7. Pulses detected for LMS 291

movements C.

Figure 8. Pulses detected for LMS-291

movements A and B.

Real widths measures given by manufacturers are:

Nissan Note= 1691 millimeters.

Peugeot 307= 1762 millimeters.

Test conclusions:

As it is shown, width accuracy is very similar for both sensors, but LMS 291 gives better

results at long distances. And the number of points detected for the desired configuration

gives also very similar configuration. Although laser radar LRS 1000 gives results in

distances higher than 80 meters.

LRS-1000 gives higher capabilities which make it a very interesting sensor for long range

detections, due to its wide vision range and its extremely high resolution. Its lower

frequency and long distance measurements makes that more structured environments, with

less changing conditions, and where long distances are important, are typically the best

scenarios for this kind of LIDAR. Typically interurban scenario matches with these

requirements. Where detections has to be done at longs distances ( > 200 meters ) due to

the speeds of the cars involved. Closer cars, on the other hand, usually do not perform

special trajectories to be tracked, so real time tracking is not so important, but long distance

detections are.

The experiment performed with the LMS-291-S05 gives similar results, but in a lower

distance range. What makes interesting this model, in comparison to the LRS-1000, besides

its lower price, is its higher detection frequency. This lower frequency makes it a very

interesting solution, when dealing with extremely changing environments, as urban

scenarios, where usually vehicles have to deal with lower distances and fast changing

conditions. In urban scenarios, cars, bikes, pedestrian or other kind of obstacles can appear

from any direction with variable trajectories, so real time tracking is mandatory, to detect

dangerous situations and warn the driver. Thus in these scenarios a fast response is crucial,

so this sensors is the best solution in urban scenarios.

0

20

40

60

0 5 10 15

Black vehicle C movement for LRS 1000

Gray vehicle C movement for LRS 1000

Black vehicle C movement for LMS 291

Gray vehicle C movement for LMS 291

0

20

40

60

5 15 25 40 60 80

Black vehicle B movement

Black vehicle A movement

Gray vehicle B movement

Gray vehicle A movement

# of points detected

in

meters

Distance detected [m]

in meters

Distance detected [m]

in meters

# of points detected

in

meters

It also has been proved that detection can be performed even in worst case scenarios where

detected cars are black. According to figures 6, 7 and 8, points are enough to detect with

reliability even most difficult obstacles. Results also shown that it is easier detect moving

obstacles when they are performing separating movements that approaching movements.

The reason for such a special behavior is that front of the car presents a special

configuration that makes more difficult the reflex ion that la rear part. Radiator and other

parts of the front of the car present special reflexions that makes harder laser detection.

4.- MOVING VEHICLE DETECTION ALGORITHM

LIDAR main disadvantage is the relative low information provided, but enough to give a

first estimation of the shape of the obstacle detected and even provide moving vehicle and

pedestrian detection.

An application has been developed in the scope of this test to provide low obstacle

detection and identification [14]. The algorithm consists on a low level detection and

identification, and higher level tracking. This tracking stage not only records and predicts

the movement of the vehicles and pedestrian, also is useful to give a more accurate

detection.

The detection points given by the laser are merged together and labeled, representing

different obstacles (Figure 9). Polylines are created to join the different points that represent

the obstacles [9]. After some line merging, each obstacle is represented by a single polyline

proportional to its shape. 5 sets of obstacles were defined (L Shaped, Fixed Obstacle,

Road Borders, Possible Pedestrian and Moving Obstacle); each of them represents

different objects that can be differentiated in road environment using the information

provided by laser radar.

Figure 9. Different Obstacle Classification.

Moving obstacles

The pattern given by these kinds of obstacles makes possible detect and track their

movement. This obstacle can be detected using LIDAR LMS 291 and its special behavior.

For 0.25 º detection, it performs 4 scans independently which give 4 sets of spots with 1º of

resolution. Each scan is separated 0.25º in relation with the previous one. So after 4 scans,

the LIDAR returns a complete set of spots separated 0.25º. When a moving obstacle is

found, the four scans performed by the LIDAR for a single detection appear with a

variation which is proportional to the speed and direction of the detected object and the test

vehicle. Measuring the distance between two consecutive points we can calculate the speed

of the car in m/s (Figure 10).

where T is the rotation period which is T=13msecs. As the number of scans is 4, three

speeds can be measured in order to provide a more reliable measure.

3

12

t

yy

Vy

nnN

Nn

, 3

12

t

xx

Vx

nnN

Nn

,

, where t=13 msegs and v is in

m/s.

Figure 10. Moving vehicle Pattern.

False positives can be avoided detecting impossible speeds or movements. LRS

information cannot give this pattern in a single spot detection, but by combining more than

one scan a similar pattern can be detected.

Tracking stage, computes the speed of the moving vehicle, first using the low level

calculated speed and lately using the high level velocity calculated using this higher level

tracking. The algorithm calculated the position of the car in the subsequent detection scan

and search within a given window for another obstacle whether moving or any other

obstacle. Moving obstacle movements are recorded. After some scans it is checked if the

car is actually moving or not and higher level classification is performed according to the

movement of the car. The higher level classification algorithm is based in a voting scheme

that uses the ten latest movements and low level classification to perform the final decision:

,

where Vi represents the number of votes for each kind of obstacle, and is the gain factor

for each obstacle, which is only different for road borders and moving obstacles.

Finally there is a correction factor that corrects the low level classification, if it was not

considered moving obstacle in low level detection and the detection window finds it inside

a moving window, the low level detection is corrected only if real movement is detected in

the obstacle within the last two sequences.

Window width= Window height =

Where K1, K2, Th1 and Th2 are configurable.

Test Performed:

Several experiments were performed to test the proposed method, a GPS sensor ASTECH

G12 GPS and a speed sensor CORREVIT L-CE were used to increase the accuracy of the

test.Some result of the test performed are shown in Figure 11. It shows the percentage of

moving obstacles to be detected according to the distance. Two movements were tested, a

vehicle moving in direction to the LIDAR (approaching movement), and in the opposite

direction (separating movement):

Figure 11 Test Performed to check the algorithm.

Experimental results are shown in figure 12 and 13:

Figure 12 Classification Results

Percentage of detection vs distance in

meters.

Figure 13: Detection Percentage. vs.

Distance in meters. Overall Results.

The probability of being detected is different if the car is separating or approaching. The

best probability of being detected is when the car is separating; mainly because the back of

the car is bigger than the front part of the car, so the detected part of the car has more

surfaces to be detected by the sensor avoiding errors due pitching movements.

The results presented in Figure 13 only focuses in the probability of moving obstacles to be

detected in a single scan. A subsequent integration along time with a tracking algorithm has

lead to a much better detection, been able to track vehicles with no misdetections.

Results given by Figure 14 shows that a car is detectable within 30/40 meters in

approaching movements and until 80 meters in separating movements, this lead to a reliable

algorithm which is able to track the movement of cars in short distance. Given the

frequency of the LIDAR sensor used (LMS 291) and detection ratio given in this test, the

application presented here proved to be very accurate. It is especially in urban

environments, where short distances and fast movements are common and speeds lower so

a car detection within 40/30 meters can help to warn drivers in case of hazardous situations.

Figure 14 Tracking, distance in meters since the moving car is detected.

5.- CONCLUSION AND FUTURE WORKS

The first conclusion that can be obtained is that LIDAR LMS-291 provides less detection

performance. Enough to give a good estimation of the surroundings of road environments,

0

50

100

82 67 52 37 22 7

B

A

D

C

F 0

50

100

80 68 56 44 32 20 8

separating movement

approaching movement

0

20

40

60

80

100

B A D C F G E H

% of detections

in

meters

Distance [m]

Distance [m] % of

detections

in

meters

Distance where tracking starts [m]

in meters

Movement

Approaching

movement

Separating

movement

but ineffective in long range detections, since it is not able to detect object over 80m in

distance. However its low cost and high frequency makes it a very interesting option for

low cost ADAS application.

LIDAR LRS-1000 is a good sensor for road environments mainly in interurban areas where

speeds are high and detections should be done in long range distances. In urban areas both

sensors are able to give information enough to give shape estimation and movement

detection, but acquisition frequency may be taken into account.

The algorithm is based in the operation principle of the LMS-291. But it can be used with

the LRS-1000 by integrating several scans and look for the pattern variation as it is done

with the LMS-291.

6.- ACKNOWLEDMENTS

The work reported in this paper has been partly funded by the Spanish Ministry of Science

and Innovation (SIAC project TRA2007-67786-C02-01, TRA2007-67374-C02-01 and

TRA2007-67786-C02-02) and the CAM project SEGVAUTO.

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