Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communication

1. Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Enhancing Automatic Incident Detection Communica- tion Techniques through Vehicle to Infrastructure Communication Introduction The proposed technique Automatic M. Abuelela , S. Olariu and Y. Gongjun incident detection The 11th International IEEE Conference on Intelligent Transportation Systems Simulation results October 5, 2008

2. Outline Enhancing Automatic Incident Detection Techniques through Vehicle to 1 Introduction Infrastructure Communica- tion 2 The proposed technique Introduction The proposed technique Automatic 3 Automatic incident detection incident detection Simulation results 4 Simulation results

3. Introduction Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Congested highways due to trafﬁc incidents cost over Introduction 75 billion a year in lost worker productivity, over 8.4 The proposed technique billion gallons of fuel and an average of 119 persons Automatic died each day in motor vehicle accidents incident detection Simulation results

4. Introduction Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Congested highways due to trafﬁc incidents cost over Introduction 75 billion a year in lost worker productivity, over 8.4 The proposed technique billion gallons of fuel and an average of 119 persons Automatic died each day in motor vehicle accidents incident detection Many incident detection algorithms exist from simple Simulation results pattern recognition to AI techniques.

5. Introduction Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Congested highways due to traffic incidents cost over Introduction 75 billion a year in lost worker productivity, over 8.4 The proposed technique billion gallons of fuel and an average of 119 persons Automatic died each day in motor vehicle accidents incident detection Many incident detection algorithms exist from simple Simulation results pattern recognition to AI techniques. The most widely-used devices are Inductive Loop Detectors that measure traffic flow by registering a signal each time a vehicle passes over them

6. Limitations of current techniques Enhancing Automatic All of the techniques, that use Inductive Loop Detectors Incident Detection or video detection cameras assign cars a passive role Techniques through in the detection process Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic incident detection Simulation results

7. Limitations of current techniques Enhancing Automatic All of the techniques, that use Inductive Loop Detectors Incident Detection or video detection cameras assign cars a passive role Techniques through in the detection process Vehicle to Infrastructure It is fundamentally difﬁcult to detect non-blocking Communica- tion accidents , as the deviation from normal trafﬁc patterns may be negligible. Introduction The proposed technique Automatic incident detection Simulation results

8. Limitations of current techniques Enhancing Automatic All of the techniques, that use Inductive Loop Detectors Incident Detection or video detection cameras assign cars a passive role Techniques through in the detection process Vehicle to Infrastructure It is fundamentally difﬁcult to detect non-blocking Communica- tion accidents , as the deviation from normal trafﬁc patterns may be negligible. Introduction Vision techniques fail under many situations like fog, The proposed heavy rain and bright sun at which most accidents technique happen. Automatic incident detection Simulation results

9. Limitations of current techniques Enhancing Automatic All of the techniques, that use Inductive Loop Detectors Incident Detection or video detection cameras assign cars a passive role Techniques through in the detection process Vehicle to Infrastructure It is fundamentally difﬁcult to detect non-blocking Communica- tion accidents , as the deviation from normal trafﬁc patterns may be negligible. Introduction Vision techniques fail under many situations like fog, The proposed heavy rain and bright sun at which most accidents technique happen. Automatic incident Moreover, installing cameras all over the highway is detection Simulation very costly. results

10. Limitations of current techniques Enhancing Automatic All of the techniques, that use Inductive Loop Detectors Incident Detection or video detection cameras assign cars a passive role Techniques through in the detection process Vehicle to Infrastructure It is fundamentally difﬁcult to detect non-blocking Communica- tion accidents , as the deviation from normal trafﬁc patterns may be negligible. Introduction Vision techniques fail under many situations like fog, The proposed heavy rain and bright sun at which most accidents technique happen. Automatic incident Moreover, installing cameras all over the highway is detection Simulation very costly. results Relying on cell phone calls still has some problems because minor events (breakdowns which occur with greater frequency and do not present a hazard to other motorists or some obstacles that block only a single lane) are often not reported by other motorists.

11. The proposed technique Enhancing Automatic Incident Although VANETs started mainly for safety Detection Techniques applications, surprisingly a very few work have been through Vehicle to done in VANETs for Automatic Incident Detection while Infrastructure Communica- most of the research went for developing routing tion protocols and privacy techniques. Introduction The proposed technique Automatic incident detection Simulation results

12. The proposed technique Enhancing Automatic Incident Although VANETs started mainly for safety Detection Techniques applications, surprisingly a very few work have been through Vehicle to done in VANETs for Automatic Incident Detection while Infrastructure Communica- most of the research went for developing routing tion protocols and privacy techniques. Introduction the proposed technique is not a replacement for any of The proposed the current AID techniques technique Automatic incident detection Simulation results

13. The proposed technique Enhancing Automatic Incident Although VANETs started mainly for safety Detection Techniques applications, surprisingly a very few work have been through Vehicle to done in VANETs for Automatic Incident Detection while Infrastructure Communica- most of the research went for developing routing tion protocols and privacy techniques. Introduction the proposed technique is not a replacement for any of The proposed the current AID techniques technique it can work beside any of them as a great enhancement Automatic incident to recover their limitations specially in sparse trafﬁc. detection Simulation results

14. The proposed technique Enhancing Automatic Incident Although VANETs started mainly for safety Detection Techniques applications, surprisingly a very few work have been through Vehicle to done in VANETs for Automatic Incident Detection while Infrastructure Communica- most of the research went for developing routing tion protocols and privacy techniques. Introduction the proposed technique is not a replacement for any of The proposed the current AID techniques technique it can work beside any of them as a great enhancement Automatic incident to recover their limitations specially in sparse trafﬁc. detection Simulation We assume that some form of infrastructures are results installed along the highway every mile or so.

15. The proposed technique Enhancing Automatic Incident Although VANETs started mainly for safety Detection Techniques applications, surprisingly a very few work have been through Vehicle to done in VANETs for Automatic Incident Detection while Infrastructure Communica- most of the research went for developing routing tion protocols and privacy techniques. Introduction the proposed technique is not a replacement for any of The proposed the current AID techniques technique it can work beside any of them as a great enhancement Automatic incident to recover their limitations specially in sparse trafﬁc. detection Simulation We assume that some form of infrastructures are results installed along the highway every mile or so. This infrastructure may be in the form of roadside or in the form of sensor belts embedded in the asphalt . roadsides

16. Vehicle and roadside model Enhancing Automatic All vehicles are assumed to be GPS enabled. Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic incident detection Simulation results

17. Vehicle and roadside model Enhancing Automatic All vehicles are assumed to be GPS enabled. Incident Detection A vehicle can detect change lanes. Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic incident detection Simulation results

18. Vehicle and roadside model Enhancing Automatic All vehicles are assumed to be GPS enabled. Incident Detection A vehicle can detect change lanes. Techniques through Vehicle to A roadside is responsible for collecting and managing Infrastructure Communica- Information about lane changes from passing vehicles tion Introduction The proposed technique Automatic incident detection Simulation results

19. Vehicle and roadside model Enhancing Automatic All vehicles are assumed to be GPS enabled. Incident Detection A vehicle can detect change lanes. Techniques through Vehicle to A roadside is responsible for collecting and managing Infrastructure Communica- Information about lane changes from passing vehicles tion Each roadside has a table called RoadImage[m][n] where m is equivalent to number of lanes and n is Introduction equivalent to the distance between two consecutive The proposed technique roadsides Automatic incident detection Simulation results

20. Vehicle and roadside model Enhancing Automatic All vehicles are assumed to be GPS enabled. Incident Detection A vehicle can detect change lanes. Techniques through Vehicle to A roadside is responsible for collecting and managing Infrastructure Communica- Information about lane changes from passing vehicles tion Each roadside has a table called RoadImage[m][n] where m is equivalent to number of lanes and n is Introduction equivalent to the distance between two consecutive The proposed technique roadsides Automatic The purpose of this table is to allow roadsides to have a incident detection virtual view about recent history of the road by Simulation results recording how many cars have passed recently over each position

21. Vehicle and roadside model Enhancing Automatic All vehicles are assumed to be GPS enabled. Incident Detection A vehicle can detect change lanes. Techniques through Vehicle to A roadside is responsible for collecting and managing Infrastructure Communica- Information about lane changes from passing vehicles tion Each roadside has a table called RoadImage[m][n] where m is equivalent to number of lanes and n is Introduction equivalent to the distance between two consecutive The proposed technique roadsides Automatic The purpose of this table is to allow roadsides to have a incident detection virtual view about recent history of the road by Simulation results recording how many cars have passed recently over each position For example, when we say that RoadImage[i][j] = x, this means that x cars have passed over the location (lane = i; position = j) in the previous time interval

22. Basic idea Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction if an incident occurred, we would expect to have a The proposed technique negative peak in the row corresponding to the lane Automatic containing the incident while other lanes are still normal incident detection or have positive peaks especially for lanes adjacent to Simulation the incidents lane results

23. Basic idea Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction if an incident occurred, we would expect to have a The proposed technique negative peak in the row corresponding to the lane Automatic containing the incident while other lanes are still normal incident detection or have positive peaks especially for lanes adjacent to Simulation the incidents lane results So, one may argue that detecting an incident is simply to detect such a peak in the RoadImage table where the minimum point, if large peak found, represents the position of the incident.

24. Problems with the Basic idea Enhancing Automatic Although this idea is simple and easy to implement, it has Incident Detection many shortcomings. Techniques through Vehicle to Infrastructure Consider this example where Communica- tion the shadowed area represents car’s path that is Introduction just received by a roadside. The proposed technique Automatic incident detection In ﬁgure a, the middle position of lane 1 has a very low Simulation value, very few cars passed through these positions results recently

25. Problems with the Basic idea Enhancing Automatic Although this idea is simple and easy to implement, it has Incident Detection many shortcomings. Techniques through Vehicle to Infrastructure Consider this example where Communica- tion the shadowed area represents car’s path that is Introduction just received by a roadside. The proposed technique Automatic incident detection In figure a, the middle position of lane 1 has a very low Simulation value, very few cars passed through these positions results recently If we applied the basic filling algorithm to the new information, then we would have the table shown in figure b.

26. Problems with the Basic idea Enhancing Automatic Incident Detection The suspected positions still have very low values Techniques through relative to corresponding positions in the other two Vehicle to Infrastructure lanes that make it still be suspected. Communica- tion However, having a car recently passed over these positions should override previous history for them and Introduction remove any suspicion accumulated over time about The proposed technique them !! Automatic Example on the above situation is when we have a slow incident detection car or temporary broken car, then a history might be Simulation built against some positions results The problem is that it would take very long time until the table be balanced again even if one car was enough to remove any suspicion about these positions.

30. Modified table filling Enhancing Automatic Incident The following rule is used to fill the RoadImage table Detection Techniques after receiving a new information from a car through Vehicle to If a car passed over a certain position, this position is Infrastructure Communica- clear and must have value larger than corresponding tion positions in other lanes Introduction The main advantage of the modified approach is that it The proposed has a rapid convergence, Once a position is cleared, technique the table will show that immediately. Automatic incident detection Also, after clearing an incident, once a car passed over Simulation the incident position, the table will show an results incident-free status. Values in the table now does not reflect number of cars passed though every position as before. They just reflect status of the road.

35. Problems with the modified table filling Enhancing Automatic If a roadside received information1 from car x at time t1 Incident Detection and received information2 from car y at time t2 where Techniques through t1 < t2, we assumed implicitly that x was always ahead Vehicle to Infrastructure of y since the last roadside Communica- tion That is not true as a general case as cars may accelerate and pass each other. Introduction The simplest example for this situation is when a slow The proposed car passes over a certain position then an accident technique occurs at that location. Automatic incident Fast cars may arrive first to next roadside and provide detection Simulation some information about the incident results However, according to the modified algorithm, when the slow car arrives at the roadside, that roadside may, wrongly, clear that position and gives it high value which is not true

41. Time dependent modiﬁed ﬁlling Enhancing Automatic Incident Detection we modify the RoadImage table to contain not only the Techniques through counter for each cell but also the last time when that Vehicle to Infrastructure counter was changed. Thus, each cell in the table will Communica- tion be on the form < Count; LTime > Whenever an information reports that its car passed Introduction over any position, we check the reported time with the The proposed technique last time stored in the table for that position Automatic If the current reported time is larger than the last time incident detection stored in the cell or the reported time is smaller than Simulation the last time by certain threshold, then we change it as results before. Otherwise, we simply ignore that result because it is outdated and should not override newer reports

45. Incident detection Enhancing Automatic the detection process may be summarized as follow Incident Detection Techniques through Vehicle to Infrastructure Compute the average (µ) and Communica- slandered deviation (σ)for tion Count values for each row in Introduction the table. i.e. for each lane. The proposed technique Automatic incident Find the minimum Count, Countmin detection Simulation Use the idea of bandpass ﬁlter to take away regular results oscillation and ﬂuctuation from the values. If µ − σ − LCountmin > K then raise an alarm for an incident, where K is a conservatively factor that determines how conservative should the detection be.

48. Effect conservatively factor Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique For small conservatively factor, more false alarms are Automatic generated. incident detection Small conservatively factor means perfect detection Simulation rate as the roadside can simply deduce the incident results during its occurrence time. As the roadside becomes more conservative, longer time will be needed to have large difference between values in the RoadImage table and in turn to detect the accident

51. Impact of traffic flow Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic The larger the traffic flow, the more reports that will be incident detection collected by the roadside and hence the larger the Simulation possibility of wrong detection. results As the traffic flow increases, more cars and drivers will be available to report about the incident and hence less detection time is required.

52. Impact of traffic flow Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic The larger the traffic flow, the more reports that will be incident detection collected by the roadside and hence the larger the Simulation possibility of wrong detection. results As the traffic flow increases, more cars and drivers will be available to report about the incident and hence less detection time is required.

53. Impact of the distance between roadsides Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction We could get 100% detection rate for roadside intervals The proposed technique less than 4500 meters for both sparse and moderate Automatic trafﬁc ﬂow. However, after 4500 meters, time needed to incident detection detection the incident is longer than the incident Simulation duration and thus, the roadside can not detect it results The larger the distance between roadsides, the longer the time needed to detect an incident. This is because, cars will need to travel longer in order to provide their reports to next roadside

54. Impact of the distance between roadsides Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction We could get 100% detection rate for roadside intervals The proposed technique less than 4500 meters for both sparse and moderate Automatic trafﬁc ﬂow. However, after 4500 meters, time needed to incident detection detection the incident is longer than the incident Simulation duration and thus, the roadside can not detect it results The larger the distance between roadsides, the longer the time needed to detect an incident. This is because, cars will need to travel longer in order to provide their reports to next roadside

55. Impact of the probability of successful communication Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic Cars may not have enough time to setup a incident detection communication with the roadside. Simulation results The detection time at success probability of 0.8 is only 10 % more than the detection time at a perfect situation Even if not all cars have succeeded in communicating with the roadside , the mean detection time may be still acceptable

58. Conclusion and future work Enhancing Automatic Incident Detection Techniques Traditional AID techniques that rely on ILDs or video through camera detection have many shortcomings Vehicle to Infrastructure Communica- We introduced a novel approach to detect non sever tion incident under non dense traffic through vehicles to infrastructure communication Introduction The proposed Future work includes developing a comprehensive technique technique to detect incidents under any traffic condition. Automatic incident More data mining and intelligent techniques may be detection used to enhance the performance of the proposed Simulation results technique Also, other traffic parameters and drivers input may be taken into consideration

63. Thank You ! Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communica- tion Introduction The proposed technique Automatic incident detection Simulation results

Enhancing Automatic Incident Detection Techniques through Vehicle to Infrastructure Communication

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