Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

Abstract

The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving.

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

“…• State machines [102,185,215,239] • Fuzzy theory [102,216] • Static BNs [122,128,147,198,216,228] • DBNs [3,58,88] -HMMs [28,142,169] -Jump Markov Models [268] • Dempster-Shafer theory [185,191,250] • Special kinds of logics [97,103,111] • Various classifiers [39,95,125] In the following, representatives of the different groups are explained to make the diversity of the methods graspable. Methods trying to infer driver intentions without any physical vehicle motion indication, e.g.…”

“…Submodels corresponding to early maneuver stages are matched online, which allows an early maneuver recognition. HMMs are also employed for the detection of turns, overtaking, and straight motion in [142] in a hierarchical way. For each high-level behavior in the upper layer HMM, a corresponding lower layer HMM represents the sequence of finer state transitions of a single behavior.…”

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios

“…In [16], the authors predicted the probability of a collision considering the ego vehicle trajectory and the predicted trajectory in the planning and prediction horizon, which was applied in the intersection scenarios. In [15], Laugier et al used the on-board sensors to analyze and interpret the dynamic scenes, and the collision risks were estimated by dealing with uncertainties from sensors. The collision risks were predicted with the use of hidden Markov models and Gaussian processes.…”

“…However, these methods did not take future potential risks or uncertainties into consideration. In addition, there have been some efforts expended on the uncertainty, as well as the environment prediction [15], but they failed to assess risks for a long term or risks beyond the prediction horizon for IAVs.…”

Risk assessment at road intersections: Comparing intention and expectation

“…The authors of [11], [12] used an evolution of the Rapidlyexploring Random Tree algorithm to generate possible future trajectories, after identifying maneuver intentions using a Support Vector Machine in [11] and Gaussian Processes in [12]. In [13] the maneuver intention is estimated using a Hierarchical Hidden Markov Model, and potential trajectories are modeled by Gaussian Processes. The first limitation of these methods is the computational cost of calculating all the possible trajectories and the pairwise probabilities that they intersect.…”