Joint ego-motion and road geometry estimation

Abstract

We provide a sensor fusion framework for solving the problem of joint ego-motion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.

Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation

“…Furthermore, the state variance, by means of the 90% confidence interval, is illustrated by gray lines or cyan colored ellipses, respectively. The estimate of the lane markings (40), illustrated by the gray dashed lines and derived according to the method presented in [47], is shown here as a comparison. We also show the tracked vehicle in front of the ego vehicle illustrated by a blue square.…”

“…The noise term of the sensor measurementz Es was defined in (11). Equation (47) can now be rewritten according to…”

“…In our scenario, radar echoes typically stem from delineators or guardrails, which we would like to track as points or polynomials, respectively, with the framework presented in Section V. We use the polynomial model (4) with n = 2 for our extended sources. This model has also been used for sequentially estimating similar parameters for the road's white lane markings in [47].…”

“…where x E and y E are expressed in the ego vehicle's coordinate frame E. The lane tracker we use is described in [47] and it is based on a ego vehicle motion model which estimates the lane given measurements from a camera, an inertia sensor and the motion of other vehicles tracked by the radar. This implies that the camera easily can be removed and that the prior solely is based on the motion model of the ego vehicle and other moving or stationary objects tracked by the radar.…”

Stereo vision-based road estimation assisted by efficient planar patch calculation

“…Since, leading vehicles are likely to follow the lane, estimation of the lane geometry can be improved. In [14], information from the Internal Measurement Unit (IMU) and steering angle is also integrated. In [8], apart from lane markings and leading vehicles, road barriers are also integrated to the filtering.…”