This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent. We construct a model, given camera images as input, that is capable of learning and predicting the dynamics of obstacles, cars and pedestrians, and train our policy using this model. Two policies that control both braking and steering are compared against a baseline where the only action taken is (conventional) braking in a straight line. The two policies are trained using two distinct reward structures, one where any and all collisions incur a fixed penalty, and a second one where the penalty is calculated based on already established delta-v models of injury severity. The results show that both policies exceed the performance of the baseline, with the policy trained using injury models having the highest performance.
Various metrics are being employed in research as a measure of crash severity, while this makes for grim reading they do provide widely adopted quantitative, data driven models of accident outcomes. They include the Acceleration Severity Index (ASI), Occupant Impact Velocity (OIV) and Delta-V. However, @cite_19 show that the former two do not offer significant predictive advantage over the latter. Since its emergence in the 1970s, Delta-V has been the traditional metric for crash severity and is defined as the absolute change between pre-collision velocity and post-collision velocity, with the assumption that larger differences in velocities are correlated with more severe injuries ( @cite_9 ):
@cite_19 @cite_9
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The occupant impact velocity (OIV) and acceleration severity index (ASI) are competing measures of crash severity used to assess occupant injury risk in full-scale crash tests involving roadside safety hardware, e.g. guardrail. Delta-V, or the maximum change in vehicle velocity, is the traditional metric of crash severity for real world crashes. This study compares the ability of the OIV, ASI, and delta-V to discriminate between serious and non-serious occupant injury in real world frontal collisions. Vehicle kinematics data from event data recorders (EDRs) were matched with detailed occupant injury information for 180 real world crashes. Cumulative probability of injury risk curves were generated using binary logistic regression for belted and unbelted data subsets. By comparing the available fit statistics and performing a separate ROC curve analysis, the more computationally intensive OIV and ASI were found to offer no significant predictive advantage over the simpler delta-V. Delta-V (Δv) is a measure of the severity of a traffic collision, defined as the change in velocity between pre-collision and post-collision trajectories of a vehicle. Delta-V emerged in the 1970s in the context of crash reconstruction analysis, and is considered by some researchers to be the best single predictor of crash severity. However, this indicator has not been applied to the analysis of traffic conflicts, until recently when it was incorporated into the automated conflict analysis algorithms of the Surrogate Safety Assessment Model (SSAM). This paper introduces Delta-V and demonstrates how it overcomes shortcomings present in several traditional measures of traffic conflict severity. We discuss the ambiguity present in the literature on the topic of traffic conflict severity, and suggest the adoption of alternative terminology and definitions. We demonstrate a new approach, incorporating Delta-V, to estimate the collision propensity and potential collision severity of a traffic conflict. There have been many studies that have documented the application of crash severity models to explore the relationship between accident severity and its contributing factors. Although a large amount of work has been done on different types of models, no research has been conducted about quantifying the sample size requirements for crash severity modeling. Similar to count data models, small data sets could significantly influence model performance. The objective of this study is therefore to examine the effects of sample size on the three most commonly used crash severity models: multinomial logit, ordered probit and mixed logit models. The study objective is accomplished via a Monte-Carlo approach using simulated and observed crash data. The results of this study are consistent with prior expectations in that small sample sizes significantly affect the development of crash severity models, no matter which type is used. Furthermore, among the three models, the mixed logit model requires the largest sample size, while the ordered probit model requires the lowest sample size. The sample size requirement for the multinomial logit model is located between these two models. In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS). We show that standard (linearized) estimation approaches, such as the Extended Kalman Filter (EKF), can fundamentally alter the system observability properties, in terms of the number and structure of the unobservable directions. This in turn allows the influx of spurious information, leading to inconsistency. To address this issue, we propose an Observability-Constrained VINS (OC-VINS) methodology that explicitly adheres to the observability properties of the true system.We apply our approach to the Multi-State Constraint Kalman Filter (MSC-KF), and provide both simulation and experimental validation of the effectiveness of our method for improving estimator consistency.