Browse Topic: Crash prevention
This SAE Standard provides minimum requirements and performance criteria for devices to prevent runaway snowmobiles due to malfunction of the speed control system.
Provizio promises its 5D Perception stack can safely compete with expensive lidar sensors at a fraction of the cost. “Safety first” is more than a catchphrase. For sensing company Provizio, it's the only way the transportation industry should introduce autonomous vehicles. In Provizio's view, using AV building blocks - technology such as automatic emergency braking and lane-keep assist - can be valuable in ADAS systems, but they should not be used to drive vehicles until the perception problem has been solved. “It's not that we're skeptical about autonomous driving, it's just that we strongly believe that the industry has taken this wrong path,” Dane Mitrev, machine learning engineer at Provizio, told SAE Media at September 2023's AutoSens Brussels conference. “The industry has looked at things the other way around. They tried to solve autonomy first, without looking at accident prevention and simpler ADAS systems. We are building a perception technology which will first eliminate road
ABSTRACT In order to expedite the development of robotic target carriers which can be used to enhance military training, the modification of technology developed for passenger vehicle Automated Driver Assist Systems (ADAS) can be performed. This field uses robotic platforms to carry targets into the path of a moving vehicle for testing ADAS systems. Platforms which are built on the basis of customization can be modified to be resistant to small arms fire while carrying a mixture of hostile and friendly pseudo-soldiers during area-clearing and coordinated attack simulations. By starting with the technology already developed to perform path following and target carrying operations, the military can further develop training programs and equipment with a small amount of time and investment. Citation: M. Bartholomew, D. Andreatta, P. Muthaiah, N. Helber, G. Heydinger, S. Zagorski, “Bringing Robotic Platforms from Vehicle Testing to Warrior Training,” In Proceedings of the Ground Vehicle
Volvo calls its all-new EX90 SUV the safest and most technically adept model in the company's 95-year history, which includes such achievements as the world's first three-point automotive seat belt in 1959. Even before this luxury EV logs its first mile on global roads that take more than 1 million human lives every year, Volvo asserts the EX90 will eliminate up to one in five serious injury accidents, and one in 10 accidents overall. That claim is based not on fuzzy math, said Lotta Jakobsson, a 33-year company veteran and specialist in injury protection, but on Volvo's industry-unique accident database that's been a wellspring of company safety innovations since the 1970s.
Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.
This paper intends to present a novel optimal trajectory planning method for obstacle avoidance on highways. Firstly, a mapping from the road Cartesian coordinate system to the road Frenet-based coordinate system is built, and the path lateral offset in the road Frenet-based coordinate system is represented by a function of quintic polynomial respecting the traveled distance along the road centerline. With different terminal conditions regarding its position, heading and curvature of the endpoint, and together with initial conditions of the starting point, the path planner generates a bunch of candidate paths via solving nonlinear equation sets numerically. A path selecting mechanism is further built which considers a normalized weighted sum of the path length, curvature, consistency with the previous path, as well as the road hazard risk. The road hazard is composed of Gaussian-like functions both for the obstacle and road boundaries, which means, if one path is near the obstacle or
With a path intrusion incident, it is almost always the case that the collision would have been avoided if the pedestrian had not run out, or if the vehicle on the minor road had stopped, or so on. However should the other party be thought to have been travelling at an excessive speed, often the reconstructionist is asked to make a calculation of what whether the collision would, at some alternative speed say equal to the speed limit, still have occurred. In that way causation is addressed. The paper distinguishes between those hazards which are distance limited and those which are time limited, giving definitions of the two types. Distance limited hazards are deterministic, but time limited hazards have a probabilistic basis. This difference has important implications for causation. For a hazard at a fixed distance, there is a well known formula for calculating whether the collision would have been avoided at a slower alternative speed. However a time limited hazard often has no clear
This recommended practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing the performance of automated driving system (ADS) during an event that meets the trigger threshold criteria specified in this document. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify a common ADS data logger record format as applicable for motor vehicle applications. The data elements defined in this document are unique to Levels 3, 4, or 5 ADS features, as defined by SAE J3016, and provide additional background of the events leading up to a crash or crash-like event. The data from sensors such as camera(s), LiDAR(s) etc. will provide information in the absence of a human driver. The data included in the ADS data logger is expected to be used in conjunction with the SAE J1698 EDR record and traditional accident reconstruction analysis. The event data recorder (EDR) and ADS data
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