Sensor error and estimation inaccuracies were found to increase the rate of false warnings more than that of undetected collisions. However, since the incidence of false alarms was small and a majority of them occurred for scenarios that were near collisions, the issue of false alarms does not appear to be a major concern in this case. It is important to note, however, that any systematic biases in sensor and estimation errors, or systematic errors in the trajectory prediction method, may increase the incidence of false alarms or undetected collisions in ways not covered by this simulation. This research may be improved in several directions. First, it would be useful to increase the complexity of the simulated overtaking maneuvers to make them more representative of overtaking scenarios. This includes considering the traffic stream beyond the three vehicles we simulated for each overtaking scenario, which brings to consideration both multi-vehicle overtaking and communication channel congestion. Analysis of the latter will require replacing our constant packet error rate with a detailed study of all sources of packet error and the techniques used to treat them, such as congestion control protocols and multi-hop broadcasting. Overtaking maneuvers that involve multiple vehicles can also be considered for driver assistance, as demonstrated in Marefat et al. (2014). Second, considering systematic biases in sensor errors and estimation inaccuracies that might occur in real-life collision warning systems will enhance our understanding of the influence of such biases on collision warning systems. Third, the analysis conducted in this study is based on simulated data, with assumptions drawn from the literature to simulate overtaking maneuvers as realistically as possible. A similar analysis with data collected from the field might help improve the assumptions made to simulate the data. Finally, this paper focuses only on a collision warning system for overtaking maneuvers. While it is useful to study such individual safety systems in isolation, it will become necessary to analyze how the we overtaking assistant simulated (or any other advanced driver assistance system) will interact with other increasingly prevalent collision warning systems. For instance, it is important to consider a warning to avoid collision of the passing vehicle with the lead vehicle (i.e., forward collision warning) while also avoiding the collision between the passing and oncoming vehicles.
Sensor error and estimation inaccuracies were found to increase the rate of false warnings more than that of undetected collisions. However, since the incidence of false alarms was small and a majority of them occurred for scenarios that were near collisions, the issue of false alarms does not appear to be a major concern in this case. It is important to note, however, that any systematic biases in sensor and estimation errors, or systematic errors in the trajectory prediction method, may increase the incidence of false alarms or undetected collisions in ways not covered by this simulation. This research may be improved in several directions. First, it would be useful to increase the complexity of the simulated overtaking maneuvers to make them more representative of overtaking scenarios. This includes considering the traffic stream beyond the three vehicles we simulated for each overtaking scenario, which brings to consideration both multi-vehicle overtaking and communication channel congestion. Analysis of the latter will require replacing our constant packet error rate with a detailed study of all sources of packet error and the techniques used to treat them, such as congestion control protocols and multi-hop broadcasting. Overtaking maneuvers that involve multiple vehicles can also be considered for driver assistance, as demonstrated in Marefat et al. (2014). Second, considering systematic biases in sensor errors and estimation inaccuracies that might occur in real-life collision warning systems will enhance our understanding of the influence of such biases on collision warning systems. Third, the analysis conducted in this study is based on simulated data, with assumptions drawn from the literature to simulate overtaking maneuvers as realistically as possible. A similar analysis with data collected from the field might help improve the assumptions made to simulate the data. Finally, this paper focuses only on a collision warning system for overtaking maneuvers. While it is useful to study such individual safety systems in isolation, it will become necessary to analyze how the overtaking assistant we simulated (or any other advanced driver assistance system) will interact with other increasingly prevalent collision warning systems. For instance, it is important to consider a warning to avoid collision of the passing vehicle with the lead vehicle (i.e., forward collision warning) while also avoiding the collision between the passing and oncoming vehicles.
CAR_STOP_overtaking_paper.docx