Signal detection theory in applied tasks: The case of hazard perception in driving

Fuzzy signal detection analysis can be a useful complementary technique to traditional signal detection theory analysis methods, particularly in applied settings. For example, traffic situations are better conceived as being on a continuum from no potential for hazard to high potential, rather than either having potential or not having potential. This study examined the relative contribution of sensitivity and response bias to explaining differences in the hazard perception performance of novices and experienced drivers, and the effect of a training manipulation. Novice drivers and experienced drivers were compared (N = 64). Half the novices received training, while the experienced drivers and half the novices remained untrained. Participants completed a hazard perception test and rated potential for hazard in occluded scenes. The response latency of participants to the hazard perception test replicated previous findings of experienced/novice differences and trained/untrained differences. Fuzzy signal detection analysis of both the hazard perception task and the occluded rating task suggested that response bias may be more central to hazard perception test performance than sensitivity, with trained and experienced drivers responding faster and with a more liberal bias than untrained novices. Implications for driver training and the hazard perception test are discussed.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.