Eric Dane dead at 53: Grey's Anatomy star dies after courageous battle with ALS... less than a year after announcing diagnosis RICHARD KAY: Andrew's fall may now be complete. The question is... Will he bring down the House of Windsor with him? Alysa Liu finally ends America's 24-year wait for a Winter Olympics figure skating gold medal as she wins nerve-shredding final The tide of sleaze rolling over Beatrice, Eugenie and Fergie is going to capsize them all. My stalker said he'd rape and dismember me. Then he turned his depraved sights on my seven-year-old daughter, says EVA LARUE.

For decades, my dad has been saying that he doesn't want to hear a word about self-driving cars until they exist fully and completely. Until he can go to sleep behind the wheel (if there is a wheel) in his driveway in western New York State and wake up on vacation in Florida (or wherever), what is the point? Driverless cars have long supposedly been right around the corner. Elon Musk once said that fully self-driving cars would be ready by 2019. Ford planned to do it by 2021.

Automated Parking Assist (APA) systems are now facing great challenges of low adoption in applications, due to users' concerns about parking capability, reliability, and completion efficiency. To upgrade the conventional APA planners and enhance user's acceptance, this research proposes an optimal-control-based parking motion planner. Its highlight lies in its control logic: planning trajectories by mirroring the parking target. This method enables: i) parking capability in narrow spaces; ii) better parking reliability by expanding Operation Design Domain (ODD); iii) faster completion of parking process; iv) enhanced computational efficiency; v) universal to all types of parking. A comprehensive evaluation is conducted. Results demonstrate the proposed planner does enhance parking success rate by 40.6%, improve parking completion efficiency by 18.0%, and expand ODD by 86.1%. It shows its superiority in difficult parking cases, such as the parallel parking scenario and narrow spaces. Moreover, the average computation time of the proposed planner is 74 milliseconds. Results indicate that the proposed planner is ready for real-time commercial applications.

We present a fast planning architecture called Hamilton-Jacobi-based bidirectional A* (HJBA*) to solve general tight parking scenarios. The algorithm is a two-layer composed of a high-level HJ-based reachability analysis and a lower-level bidirectional A* search algorithm. In high-level reachability analysis, a backward reachable tube (BRT) concerning vehicle dynamics is computed by the HJ analysis and it intersects with a safe set to get a safe reachable set. The safe set is defined by constraints of positive signed distances for obstacles in the environment and computed by solving QP optimization problems offline. For states inside the intersection set, i.e., the safe reachable set, the computed backward reachable tube ensures they are reachable subjected to system dynamics and input bounds, and the safe set guarantees they satisfy parking safety with respect to obstacles in different shapes. For online computation, randomized states are sampled from the safe reachable set, and used as heuristic guide points to be considered in the bidirectional A* search. The bidirectional A* search is paralleled for each randomized state from the safe reachable set. We show that the proposed two-level planning algorithm is able to solve different parking scenarios effectively and computationally fast for typical parking requests. We validate our algorithm through simulations in large-scale randomized parking scenarios and demonstrate it to be able to outperform other state-of-the-art parking planning algorithms.

In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configuration along a collision-free trajectory while adhering to the dynamics of the system. Specifically, we use LiDAR-based Hector SLAM for building the map of the environment, detecting obstacles, and for tracking vehicle's conformance to the trajectory as it passes through various states. For motion planning, we use rapidly exploring random trees (RRTs) on a set of generated motion primitives to search for dynamically feasible trajectory sequences and collision-free path to the goal. We demonstrate complex maneuvers such as parallel parking, perpendicular parking, and reversing motion by the real vehicle in a constrained environment using the presented approach.

You likely try to keep your car in tip-top shape. You change your oil, make sure your tires are roadworthy, give your engine regular tune-ups, etc. But what most drivers don't realize is that with today's advanced motor vehicles, that's only half the job. The most crucial elements of today's cars can't be kicked, tightened or oiled. While a quick peek under the hood may have once sufficed to ensure that everything is humming along nicely, virtually all the critical functions in today's cars that were once exclusively mechanical are now controlled by hundreds of onboard computers running tens of millions of lines of code.