(Slides) A Method for Pedestrian Position Estimation using Inter-Vehicle Communication
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This document proposes a method for pedestrian position estimation using inter-vehicle communication. The method involves pedestrians carrying beacon devices that broadcast unique IDs, and cars equipped with antennas to receive beacon signals and estimate pedestrian positions. Cars then share estimated positions with other nearby cars via packets to improve accuracy. Experiments show the method allows cars to receive sufficient position updates to detect pedestrians within stopping distance, and accuracy improves as more cars share information. Future work could evaluate pedestrian accident risk and filter low-risk pedestrians, and track positions over time.
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(Slides) A Method for Pedestrian Position Estimation using Inter-Vehicle Communication
- 1. A Method for Pedestrian Position Estimation using Inter-Vehicle Communication Yuta Sawa , Tomoya Kitani †, Naoki Shibata†† Keiichi Yasumoto , Minoru Ito Nara Institute of Science and Technology †Shizuoka University, ††Shiga University 2015/4/22 1Autonet 2008
- 2. Outline • Drivers do not notice pedestrians in blind spots • By exchanging information of pedestrian positions among cars using inter-vehicle communication, drivers can notice pedestrians in the blind spot 2015/4/22 Autonet 2008 2
- 3. Contents • Background • Assumptions • Proposed method • Experiments • Conclusion 2015/4/22 Autonet 2008 3
- 4. Background • The total number of fatal traffic accidents is decreasing in Japan[1] However, the percentage of the number of traffic accidents where a pedestrian is killed or injured has been increasing Some car manufacturing companies are developing mechanisms to detect pedestrians 2015/4/22 Autonet 2008 4 [1]National Police Agency, Japan: “Police White Paper”, http://www.npa.go.jp/hakusyo/index.htm TrafficAccidentFatalitiesNumber Percentageofpedestrians Total Traffic Accident Fatalities Number Percentage of pedestrians Pedestrian’s Traffic Accident Fatalities Number
- 5. Related Work • Some of these mechanisms employ: sensors installed on a car The problem with this method is the dead angle of the sensors • In other mechanisms, devices carried by pedestrians and cars send their positions to another kind of device installed on crosswalks [4,6] This method requires extra costs for installing the devices on crosswalks 2015/4/22 Autonet 2008 5 ○ ×
- 6. Related Work • Many car companies and researchers are studying techniques for creating a vehicle safety system using inter-vehicle communication [5] • This method …. can solve the dead angle problem achieves accurate detection does not need any extra costs for installing devices on crosswalks • No detailed implementation method or protocol is provided 2015/4/22 Autonet 2008 6
- 7. Our Goals • To achieve an avoid collision system with pedestrians estimate pedestrian’s position until the car reaches within the stopping distance estimate pedestrian’s location as accurately as possible estimate with no extra costs for installing the devices on crosswalks 2015/4/22 Autonet 2008 7
- 8. Contents • Background • Assumptions • Proposed method • Experiments • Conclusion 2015/4/22 Autonet 2008 8
- 9. Assumptions for Cars and Pedestrians • Assumptions for Pedestrians Each pedestrian has a beacon device A unique ID is assigned to each beacon device • Assumptions for Cars Each car is equipped with…. • a directional antenna capable of receiving beacon signals and estimating their directions • a GPS receiver • a computer with a certain amount of storage and accurate clock 2015/4/22 Autonet 2008 9
- 10. Assumptions for Communication • Time interval beacon signals and packets are tb [s] and tc [s] • Beacon signals Beacon signals propagates in the area with radius rb [m] centered at its sender All cars in the area can receive the beacon signal when there is no radio interference • Packets Each packet sent by a car propagates in the area with radius rc [m] All cars in the area can receive the packet when there is no radio interference 2015/4/22 Autonet 2008 10 rb rc
- 11. Assumptions for Directional Antenna • Directional antenna When receiving the pedestrian’s beacon signal, it can estimate the direction and distance from the pedestrian • Error An error in the measured direction and distance follows normal distributions 2015/4/22 Autonet 2008 11
- 12. Contents • Background • Assumptions • Proposed method • Experiments • Conclusion 2015/4/22 Autonet 2008 12
- 13. Algorithm for Detecting Pedestrians 2015/4/22 Autonet 2008 13 Construct a packet with unique beacon ID and beacon sequence number When a car receives the beacon signals, it estimates the pedestrian’s position according to measured beacon signal When a car receives the packet, it composes the information of the estimation with the existent one of the same pedestrian Time interval for sending beacon signals and packets are tb [s] and tc [s] Construct a packet with the information of the estimation The car improves the estimation of the pedestrian
- 14. Constraint of Detecting Pedestrians 2015/4/22 Autonet 2008 14 If a driver notices a pedestrian in dangerous situation This car is forced to run several ten meters since the driver decides to brake until the braking gets effective Stopping Distance Pedestrian’s position must be informed to the driver until the car reaches within the stopping distance
- 15. Pedestrian’s Existence Probability Map • Pedestrian’s Existence Probability Map From assumption, cars can calculate the error probability of angle and distance by the measured angle and distance We divide the target space in a road map into grids Cars calculate each grid probabilistic index for the central point of the grid by using the error probability 2015/4/22 Autonet 2008 15
- 16. Composing Multiple Probability Maps • The measurement data has to satisfy the following condition All cars receive the same beacon with the same ID transmitted at the same time 2015/4/22 Autonet 2008 16 1 11
- 17. Contents • Background • Assumptions • Proposed method • Experiments • Conclusion 2015/4/22 Autonet 2008 17
- 18. Experiments • Evaluation Items Number of Received Packets To evaluate the estimation accuracy, we measured how many packets car receives until the car reaches within the stopping distance Estimation Accuracy of Pedestrian’s Location we measured the radius of the smallest circle which contains all cells of the grid with over 90% existence probability 2015/4/22 Autonet 2008 18
- 19. Parameter of Experiments • We have used QualNet Simulator • Parameters 2015/4/22 Autonet 2008 19 Number of Cars in the east-west direction varies among 2, 4, 6, 8 Number of Cars in the north-south direction varies among 2, 4, 6, 8, 10 Moving Speed of All Cars 12 m/s Stopping Distance 22 m Physical Layer Protocol 802.11b Radio Propagation Range 100 m Interval of Beacon and packet Broadcasting 0.2 sec Size of Pedestrian’s Beacon 100 byte Size of packet 150 byte The Target Area : Crosswalks located at Shijo-Kawaramachi in Central Kyoto ( 142 m× 142 m ) 12 m/s 22 m
- 20. Result -Number of Received Packets • Evaluate the number of received packets We measured the number of beacons successfully received until the car reaches within the stopping distance • For all conditions Each car was able to receive more than 14 packets until the car reaches within the stopping distance 2015/4/22 Autonet 2008 20 This result shows that the number of packets is sufficient for estimating the position of the pedestrian
- 21. Result -Accuracy of Pedestrian Position • Evaluate estimation accuracy of pedestrian’s location We measured the area which contains all cells of the grid with over 90% existence probability The variance of distance error : 10, 20, 30 [m] The variance of angle error : 6 [deg] • When there is only one car 2015/4/22 Autonet 2008 21 : :
- 22. Result -Accuracy of Pedestrian Position • When there are 8 cars 2015/4/22 Autonet 2008 22 These results show that the accuracy of estimation is improved as the number of cars exchanging information increases ×6
- 23. Result -Accuracy of Pedestrian Position • We measured the radius of the smallest circle which contains all cells of the grid with over 90% existence probability • When the variance of distance error : 10 [m] 2015/4/22 Autonet 2008 23 Only one car：18.33 [m] Eight cars ： 3.74 [m] This result shows that estimation accuracy is reasonable to estimate the pedestrian’s moving direction
- 24. Conclusion • We presented a method for pedestrian position estimation using inter-vehicle communication We confirmed that cars can detect a pedestrian until the driving car reaches within the stopping distance • Future work Evaluating each pedestrian’s accident risk for each driver Filtering out low accident risk Detecting a series of pedestrian’s position for a time interval 2015/4/22 Autonet 2008 24
- 25. 2015/4/22 Autonet 2008 25 Sawa, Y., Kitani, T., Shibata, N., Yasumoto, K., Ito, M.: A Method for Pedestrian Position Estimation using Inter-Vehicle Communication, Proc. of the 3rd IEEE Workshop on Automotive Networking and Applications (AutoNet 2008), pp. 1 – 6. DOI:10.1109/GLOCOMW.2008.ECP.57 [ PDF ]
Editor's Notes
- Thank you chairman. I would like to have a talk titled a method for pedestrian position estimation using inter-vehicle communication.
- First of all, I would like to explain our study outline. Drivers do not notice pedestrians because of blind spots. By exchanging information of pedestrian positions between cars using inter-vehicle communication, drivers can notice pedestrians in the blind spot. We aim to achieve an avoid collision system with pedestrians by exchanging information of pedestrian positions among cars using inter-vehicle communication.
- This is the contents of this presentation. Now, I would like to explain the research background.
- The total number of fatal traffic accidents is decreasing in Japan. However the percentage of the number of traffic accidents where a pedestrian is killed or injured has been increasing. For the above reason, some car manufacturing companies and researchers are developing mechanisms to detect pedestrians.
- Some of these mechanisms employ sensors installed on a car. The problem with this method is dead angle of the sensors. In other mechanisms, devices carried by pedestrians and cars send their positions to another kind of device installed on crosswalks. This method requires extra costs of installing the devices on crosswalks.
- At the same time, many car companies and researchers studying techniques for creating a vehicle safety system using inter-vehicle communication. This method can solve the dead angle problem, and it achieves accurate detection. Additionally, it does not need any extra costs for installing devices on crosswalks. However, no detailed implementation method or protocol is provided
- In order to achieve an avoid collision system with pedestrians, Cars estimate pedestrian’s position until the car reaches within the constraint distance, and they estimate pedestrian’s location as accurately as possible, And they estimate with no extra costs for installing the devices on crosswalks.
- Next, I would like to explain the assumptions of our research.
- First, I would like to explain the assumptions for pedestrians. Each pedestrian has a beacon device. And a unique ID is assigned to each beacon device. Next, I would like to explain the assumptions for cars. Each car is equipped with a directional antenna capable of receiving beacon signals and estimating their directions. It is also equipped with a GPS receiver and a computer with a certain amount of storage and accurate clock.
- Next, I would like to explain the assumptions for communication. Time intervals for sending beacon signals and packets are t_b seconds and t_c seconds, respectively. Each beacon signal propagates in the area with radius r_b meters centered at its sender. Each packet sent by a car propagates in the area with radius r_c meters. All cars in the area can receive the beacons and the packet when there is no radio interference.
- Finally, I explain the assumptions for directional antenna. A directional antenna which is equipped with cars can estimate the direction and distance of its sender pedestrian When receiving the pedestrian’s beacon signal, it can estimate the direction an d distance from the pedestrian. An error in the measured direction follows normal distribution. An error in the measured distance also follows normal distribution.
- Next, I would like to explain the proposed method.
- Here, I would like to explain the outline of the proposed method. First, a beacon device which is equipped with pedestrians construct a packet with an unique beacon ID and the beacon sequence number, and broadcasts to cars. When a car receives pedestrian’s beacon signals, it estimates the pedestrian’s position according to measured beacon signal. Next, the car construct a packet with the information of the estimation, and broadcasts to other cars. When a car receives the packet, it composes the information of the estimation with the existent one of the same pedestrian. In this way, the car improves the estimation of pedestrian. The time interval for sending beacon signals and packets are t_b seconds and t_c seconds respectively.
- Next, I would like to explain the constraint of proposed method. If a driver notices a pedestrian in dangerous situation, this car is forced to run several ten meters since the driver decides to brake until the braking gets effective. We define such distance as the stopping distance. In order to achieve an avoid collision system for pedestrians, The pedestrian’s position must be informed to the driver until the car reaches within the stopping distance.
- Next, I would like to explain pedestrian’s existence probability map. From assumption, cars can calculate the error probability of angle and distance by the measured angle and distance. In our research, we divide the target space in a road map into grids in order to reduce computation time. Next, cars calculate each grid probabilistic index for the central point of the grid by using each the error probability.
- In our method, in order to improve the estimation accuracy of position, the probability maps obtained by multiple cars are composed. In order to compose multiple probability maps, the measurement data has to satisfy the following condition. All cars receive the same beacon with the same ID transmitted at the same time. In this situation, to get more accurate map, we compose multiple maps using Bayse theorem.
- Next, I will talk about experiments and their results.
- The purpose of experiments is to evaluate performance of our method. We have two evaluation items. One of them is the number of the received packets : In order to evaluate the estimation accuracy, we measured how many packets car receives until the car reaches within the stopping distance. Another one is the estimation accuracy of pedestrian’s location : we measured the radius of the smallest circle which contains all cells of the grid with over 90% existence probability.
- We have used QualNet simulator. There are parameters of the experiments. The target area is a large crosswalks located at Shijo-Kawaramachi, which is in the central Kyoto. The size of this area is 142 meters square. This crosswalks has a set of traffic signals, the signals for east-west direction is set to green, and the signals for north-south direction is red. We varied the number of cars which go through the crosswalks in the east-west direction among 2, 4, 6 and 8, and the number in the north-south direction among 2, 4, 6, 8 and 10. The moving speed of all cars on the road with green signal are 50 km/h. In this case, the stopping distance becomes 22meters. Pedestrians’ devices for transmitting beacon and the inter-vehicle communication use IEEE 802.11b as the physical layer protocol, and the radio propagation range is 100 meters. The interval of beacon and packet broadcasting is 0.2 seconds. The size of a pedestrian’s beacon is 100 bytes, and the size of data measured by a car is 150 bytes
- Here, I explain the first experiment. In order to evaluate the number of received packets, we measured the number of beacons successfully received until the car reaches within the stopping distance. The figure shows that the number of beacons successfully received until the car reaches within the stopping distance. X axis shows the Node ID and Y axis shows the number of received packets. For all conditions, the car is able to receive more than 14 packets until the car reaches within the stopping distance, which is sufficient for estimating the position of the pedestrian.
- Here, I explain the second experiment. In order to evaluate estimation accuracy of estimation pedestrian’s location , we measured the area of the grid with over 90% existence probability. This figure shows that the area of the grid with over 90% existence probability when there is only one car.
- This figure shows that the area of the grid with over 90% existence probability when there are 8 cars. These results show that the accuracy of estimation is improved as the number of cars exchanging information increase.
- Next, we measured the radius of the smallest circle which contains all cells of the grid with over 90% existence probability. X axis shows the number of cars and Y axis shows the error of pedestrian position. From this figure, when the variance for the error of distance is 10 meters, the radius is 18.33 meter when there is only one car, but the radius becomes 3.74 meters when there are 8 cars. This result shows that estimation accuracy is reasonable to estimate the pedestrian’s moving direction.
- We proposed a method for pedestrian position estimation using inter-vehicle communication. We confirmed that cars can detect a pedestrian until the driving car reaches the pedestrian. Future works are as follows.