METHOD AND SYSTEM FOR AN ACCURATE AND ENERGY EFFICIENT VEHICLE LANE DETECTION

• US 20160091609A1
• Filed: 03/18/2015
• Published: 03/31/2016
• Est. Priority Date: 09/30/2014
• Status: Abandoned Application

• Current Assignee: Umm Al-Qura University
• Original Assignee: Umm Al-Qura University
• Inventors: Basalamah, Anas, Youssef, Moustafa Amin, Ismail, Heba Allah Aly AbdEl-Halim Aly
• Application Number:
  US14/661,272
• Publication Number:
  US 20160091609A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 19/49 whereby the further system is an inertial position system, e.g. loosely-coupled

Abstract

Knowledge of the vehicle'"'"'s lane position is required for several location-based services such as advanced driver assistance systems, driverless cars, and predicting driver'"'"'s intent, among many other emerging applications. We present LaneQuest: a system and method that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the vehicle'"'"'s current lane. LaneQuest leverages the phone sensors about the surrounding environment to detect the vehicle'"'"'s lane. For example, a vehicle making a right turn most probably will be in the right-most lane, a vehicle passing by a pothole will be in a specific lane and the vehicle angular velocity when driving through a curve reflects its lane. The ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane span distribution of the different lane-level anchors.

Claims

1. A method, comprising:

gathering a raw sensor data residing in inertial sensor of a smart phone and a raw location data using a vehicle sensor to preprocess a location estimate;

applying a local weighted low pass filter method to remove a noise from the raw sensor data and corroborating with the raw location data to produce a raw sensor measurement for a lane change detection;

detecting a lane change event using a x, y and z axis measurement change in the inertial sensor of the smart phone; and

incorporating a lane anchor data gathered using a unsupervised crowd sourcing approach stored in a repository, the raw sensor measurement and the lane change event to calculate a lane position for a specific vehicle using a Markov probabilistic lane detection model without any prior assumption of a starting lane position and to provide real time data to a customer on a road hindrance at a lane level granularity.

2. The method of claim 1, further comprising:

finding the lane position in which the car is travelling based on a lane anchors pre-established in the road.

3. The method of claim 1, further comprising:

increasing the accuracy of the lane prediction through the unsupervised crowd sourcing approach; and

predicting lanes when the vehicle travels through a curve tunnel or passes by a pothole.

4. The method of claim 2, wherein the lane anchors are one of an organic lane anchor and a bootstrap lane anchor.

5. The method of claim 4, further comprising:

calculating the organic lane anchor by applying a spatial clustering on all samples collected from all users that are detected as a curves or pothole;

performing a second level of clustering for the first clustering data points based on lane-discriminating features to determine the lane position for the organic lane anchor; and

constructing a probability distribution from all the reported vehicle lane beliefs for the points within this last resulting feature based clustering step.

11. The method of claim 4, further comprising:

calculating the bootstrap lane anchor by applying a spatial clustering on all samples collected from all users that are detected as a turn, merging and exit lanes and stopping lanes;

performing a second level of clustering for the first clustering data points based on lane-discriminating features to determine the lane position for the bootstrap lane anchor; and

constructing a probability distribution from all the reported vehicle lane beliefs for the points within this last resulting feature based clustering step.

6. A system, comprising:

a preprocessing module for gathering a raw sensor data residing in an inertial sensor of a smart phone and raw location data in a sensor of a vehicle and applying a local weighted low pass filter method residing in a smart phone sensor to remove a noise from the raw sensor data and corroborating with the raw location data to produce a raw sensor measurement for a lane change detection;

an event detection module to detect events from a lane change and a lane anchor that the vehicle encounters and updating the lane change and creating a perception model for lane anchor detection and a motion update using the lane change detection;

a repository for the lane anchor are created by unsupervised crowd sensing approach that are created by a user and processed using a two stage clustering and used for the lane anchor detection; and

a probabilistic lane estimation module uses Markov probabilistic lane detection method to predict a lane estimate by using the repository, the perception model, motion update and a current user lane state to provide real time data to a customer on a road hindrance at a lane level granularity.

7. The system of claim 6, wherein the lane anchor is at least one of a bootstrap anchor or an organic anchor.

8. The system of claim 6, further comprising:

the probabilistic lane estimation module to calculate an accurate lane information is done using the Markov probabilistic lane detection method without any prior knowledge of a lane position.

10. The system of claim 6, further comprising:

the event detection module to detect a motion event and the lane anchor to feed data into the probabilistic lane estimation module.

9. (canceled)