# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 9 Deep Learning for Intention Estimation
### DL Approach  
In [181], a data-driven approach using a deep network is proposed which uses the skeleton features of the pedestrian to estimate future intention. The evaluation provided results similar to [189] in terms of classification of whether the pedestrian will cross or stop when approaching the road, but the data-driven approach in [181] is a simpler method and requires less dense information, i.e., requiring monocular information rather than stereo and dense optical flow as in [178; 189]. In other approaches, stereo cameras [191] and LiDAR [183] are used to predict pedestrian intentions based on their silhouette. Head and body orientation estimation have also been used for intention estimation for pedestrians in [190; 192; 193; 216]. [PERSON] et al [181] argue that it is unclear how these estimations provide accurate intention estimation or if they provide significant additional time for reactive manoeuvres. For example, before the collision, pedestrians typically look in the direction of the oncoming vehicle [196]. In [181] a pose estimation technique is employed and the orientation of the body and head of the pedestrian is considered as in [193]. It is suggested in [192], that head orientation is not particularly useful for pedestrians that are intending to stop or cross the road. Delays may be caused when predicting pedestrian intentions due to insufficient information on the posture and body movements [194], which also makes the data-driven approach more effective as it will use the information provided from the sensors in real time. An approach using a vision-based technique to evaluate the pose of a pedestrian over several frames to establish the risk to the pedestrian with respect to the vehicle in [181]. The approach uses a CNN-based technique to detect and estimate the pose of pedestrian based on the work in [217]. It is also mentioned in [181] that high-level features, such as skeleton joints, can provide more information than low-levels, such as HOG and Histogram of Optical Flow (HOF) [218]. The overall design used a monocular camera for a pedestrian detector and 2D pedestrian pose estimation for determining the intentions of the pedestrian [219; 220; 221], whereas [220; 221] used machine learning techniques that can also be applied using DL techniques. This technique is simpler to implement than some other pedestrian intention estimation techniques that require stereo cameras and optical flow to function. For future work, [181] suggests the application of the technique in situations where there are multiple pedestrians and with occlusion for evaluation. However, to achieve this application, a dataset would need to be produced to sufficiently evaluate the technique. In [222], a thermal sensor was used for more accurate results in low-light scenarios. The proposed method combined body features (e.g., standing, walking) with head orientation features. This method uses the distance between a pedestrian and curb (DPC), the lateral moving speed (LMS) and head orientation (HO) of the pedestrian. The approach provided the lowest error rate (22.03%) than any other combination of the features. This approach outperformed other prediction methods, such as a Markovian model [223] and a DBN approach [203]. RNNs have been used for sequence-based prediction in various applications, such as human gait analysis [224], handwriting imitation [225] and human interactions [226]. The RNN uses a feedback loop to capture temporal information by going into an internal state known as the hidden unit. RNNs allow for data to be fed back to the previous layers [183]. However, this type of RNN can become inaccurate in long-term predictions. Therefore, the RNN+LSTM architecture [207], which provides for a memory unit for the RNN in the LSTM units. The stored information values can change depending on the previous outputs and new inputs. These LSTM units consist of a cell state and four gate layers, with each gate consisting of an activation function that takes into account the current state and the previous state as an input.